This document presents a new model of categorization called Categorization by Elimination (CBE). CBE uses as few cues or features as necessary to make an accurate category assignment, unlike most existing models which use all available cues. CBE orders cues by validity and uses them sequentially, eliminating potential categories after each cue is considered until only one category remains. The authors show that CBE performs as well as humans and other algorithms on categorization tasks while using fewer cues, making it a parsimonious psychological model of fast and frugal categorization.
This document discusses various machine learning classifiers that have been used for emotion recognition from speech, including neural networks, Gaussian mixture models, linear regression, and decision trees. Neural networks are identified as the most suitable classifier for this complex problem due to their ability to learn patterns from data and model complex nonlinear relationships. The document provides details on different neural network architectures and training methods that have been employed for emotion recognition from speech in previous studies.
Main single agent machine learning algorithmsbutest
This document summarizes several machine learning algorithms and their potential applications to multi-agent systems. It describes algorithms such as decision trees, neural networks, Bayesian methods, reinforcement learning, inductive logic programming, case-based reasoning, support vector machines, and genetic algorithms. For each algorithm, it provides a brief description and discusses any existing or potential work applying the algorithm to multi-agent domains.
The document discusses the differences between machine learning (ML), statistical learning, data mining (DM), and automated learning (AL). It argues that while ML and statistical learning developed similar techniques starting in the 1960s, DM emerged in the 1990s from a merging of database research and automated learning. However, industry was much more enthusiastic about adopting DM techniques compared to AL techniques, even though many DM systems are just friendly interfaces of AL systems. The document aims to explain the key differences between DM and AL that led to DM's greater commercial success.
John Anderson was born in 1947 in Vancouver, British Columbia. He earned his PhD from Stanford University in 1972 and has been a professor at several universities, including Carnegie Mellon University since 1978. ACT* is a cognitive theory developed by Anderson that describes a spreading activation model of semantic memory combined with a production system for executing higher-level operations. It distinguishes three types of memory - declarative, procedural, and working memory - and three types of learning.
The document provides an overview of knowledge representation formalisms including semantic networks and frames. It discusses the syntax and semantics of semantic networks, how they can represent relations between concepts using nodes and links, and techniques for inference including inheritance and intersection search. Frames are also introduced as another knowledge representation technique where information about concepts is organized into objects with attributes and values.
This document provides a summary of approaches for performing sentiment analysis. It discusses document-level, sentence-level, and aspect-level sentiment analysis. At the document level, the entire document is classified as positive or negative. At the sentence level, each sentence's sentiment is determined. At the aspect level, the sentiments expressed towards specific aspects are identified. The document also outlines applications of sentiment analysis such as in e-commerce, brand/customer feedback analysis, and government use. Finally, it discusses sentiment classification approaches and levels.
Improving Tools in Artificial IntelligenceBogdan Patrut
This document provides an overview of tools used in artificial intelligence for knowledge representation. It discusses logic-based approaches like rules, frames and scripts. It also discusses search methods like breadth-first, depth-first and heuristic search. Additionally, it introduces fuzzy logic as an approach to handle non-monotonic or uncertain reasoning needed for problems in the real world. Fuzzy sets allow degrees of membership between 0 and 1 rather than classical binary set membership. Fuzzy relations can then be defined through membership functions and combined through operations like minimum.
SEMI-SUPERVISED BOOTSTRAPPING APPROACH FOR NAMED ENTITY RECOGNITIONkevig
The aim of Named Entity Recognition (NER) is to identify references of named entities in unstructured documents, and to classify them into pre-defined semantic categories. NER often aids from added background knowledge in the form of gazetteers. However using such a collection does not deal with name variants and cannot resolve ambiguities associated in identifying the entities in context and associating them with predefined categories. We present a semi-supervised NER approach that starts with identifying named entities with a small set of training data. Using the identified named entities, the word and the context features are used to define the pattern. This pattern of each named entity category is used as a seed pattern to identify the named entities in the test set. Pattern scoring and tuple value score enables the generation of the new patterns to identify the named entity categories. We have evaluated the proposed system for English language with the dataset of tagged (IEER) and untagged (CoNLL 2003) named entity corpus and for Tamil language with the documents from the FIRE corpus and yield an average f-measure of 75% for both the languages.
This document discusses various machine learning classifiers that have been used for emotion recognition from speech, including neural networks, Gaussian mixture models, linear regression, and decision trees. Neural networks are identified as the most suitable classifier for this complex problem due to their ability to learn patterns from data and model complex nonlinear relationships. The document provides details on different neural network architectures and training methods that have been employed for emotion recognition from speech in previous studies.
Main single agent machine learning algorithmsbutest
This document summarizes several machine learning algorithms and their potential applications to multi-agent systems. It describes algorithms such as decision trees, neural networks, Bayesian methods, reinforcement learning, inductive logic programming, case-based reasoning, support vector machines, and genetic algorithms. For each algorithm, it provides a brief description and discusses any existing or potential work applying the algorithm to multi-agent domains.
The document discusses the differences between machine learning (ML), statistical learning, data mining (DM), and automated learning (AL). It argues that while ML and statistical learning developed similar techniques starting in the 1960s, DM emerged in the 1990s from a merging of database research and automated learning. However, industry was much more enthusiastic about adopting DM techniques compared to AL techniques, even though many DM systems are just friendly interfaces of AL systems. The document aims to explain the key differences between DM and AL that led to DM's greater commercial success.
John Anderson was born in 1947 in Vancouver, British Columbia. He earned his PhD from Stanford University in 1972 and has been a professor at several universities, including Carnegie Mellon University since 1978. ACT* is a cognitive theory developed by Anderson that describes a spreading activation model of semantic memory combined with a production system for executing higher-level operations. It distinguishes three types of memory - declarative, procedural, and working memory - and three types of learning.
The document provides an overview of knowledge representation formalisms including semantic networks and frames. It discusses the syntax and semantics of semantic networks, how they can represent relations between concepts using nodes and links, and techniques for inference including inheritance and intersection search. Frames are also introduced as another knowledge representation technique where information about concepts is organized into objects with attributes and values.
This document provides a summary of approaches for performing sentiment analysis. It discusses document-level, sentence-level, and aspect-level sentiment analysis. At the document level, the entire document is classified as positive or negative. At the sentence level, each sentence's sentiment is determined. At the aspect level, the sentiments expressed towards specific aspects are identified. The document also outlines applications of sentiment analysis such as in e-commerce, brand/customer feedback analysis, and government use. Finally, it discusses sentiment classification approaches and levels.
Improving Tools in Artificial IntelligenceBogdan Patrut
This document provides an overview of tools used in artificial intelligence for knowledge representation. It discusses logic-based approaches like rules, frames and scripts. It also discusses search methods like breadth-first, depth-first and heuristic search. Additionally, it introduces fuzzy logic as an approach to handle non-monotonic or uncertain reasoning needed for problems in the real world. Fuzzy sets allow degrees of membership between 0 and 1 rather than classical binary set membership. Fuzzy relations can then be defined through membership functions and combined through operations like minimum.
SEMI-SUPERVISED BOOTSTRAPPING APPROACH FOR NAMED ENTITY RECOGNITIONkevig
The aim of Named Entity Recognition (NER) is to identify references of named entities in unstructured documents, and to classify them into pre-defined semantic categories. NER often aids from added background knowledge in the form of gazetteers. However using such a collection does not deal with name variants and cannot resolve ambiguities associated in identifying the entities in context and associating them with predefined categories. We present a semi-supervised NER approach that starts with identifying named entities with a small set of training data. Using the identified named entities, the word and the context features are used to define the pattern. This pattern of each named entity category is used as a seed pattern to identify the named entities in the test set. Pattern scoring and tuple value score enables the generation of the new patterns to identify the named entity categories. We have evaluated the proposed system for English language with the dataset of tagged (IEER) and untagged (CoNLL 2003) named entity corpus and for Tamil language with the documents from the FIRE corpus and yield an average f-measure of 75% for both the languages.
The document defines several key machine learning and neural network terminology including:
- Activation level - The output value of a neuron in an artificial neural network.
- Activation function - The function that determines the output value of a neuron based on its net input.
- Attributes - Properties of an instance that can be used to determine its classification in machine learning tasks.
- Axon - The output part of a biological neuron that transmits signals to other neurons.
Predicting performance in Recommender Systems - PosterAlejandro Bellogin
This document discusses predicting the performance of recommender systems. It proposes that data commonly available to recommender systems could enable estimating the success of recommendations. Specifically, it aims to 1) define a performance prediction theory for recommender systems, 2) adapt query performance techniques from information retrieval to recommendations, and 3) evaluate appropriate performance metrics. The research also explores applying these models when combining multiple recommendation strategies or hybrid recommender systems.
Heterogeneous Proxytypes as a Unifying Cognitive Framework for Conceptual Rep...Antonio Lieto
This document summarizes Antonio Lieto's work on developing a cognitive framework called heterogeneous proxytypes for conceptual representation and reasoning in artificial systems. The framework incorporates multiple knowledge representations, including prototypes, exemplars, and theories. It allows different representations and reasoning mechanisms to be activated based on context. Lieto describes cognitive models that integrate heterogeneous proxytypes, like the DUAL-PECCS system, and evaluates them on commonsense reasoning tasks.
Trajectory Data Fuzzy Modeling : Ambulances Management Use Caseijdms
Data captured through mobile devices and sensors represent valuable information for organizations. This
collected information comes in huge volume and usually carry uncertain data. Due to this quality issue
difficulties occur in analyzing the trajectory data warehouse. Moreover, the interpretation of the analysis
can vary depending on the background of the user and this will make it difficult to fulfill the analytical
needs of an enterprise. In this paper, we will show the benefits of fuzzy logic in solving the challenges
related to mobility data by integrating fuzzy concepts into the conceptual and the logical model. We use the
ambulance management use case to illustrate our contributions.
Methods of Combining Neural Networks and Genetic AlgorithmsESCOM
1. The document discusses methods for combining neural networks and genetic algorithms. It describes three main approaches: evolving connection weights, evolving network architectures, and evolving learning rules.
2. In the first approach, a genetic algorithm is used as the learning rule to optimize neural network weights. The second approach uses a genetic algorithm to select structural parameters of the network and trains the network separately. The third approach evolves parameters of the network's learning rule.
3. Combining neural networks and genetic algorithms can help compensate for each techniques' weaknesses in search, and may lead to highly successful adaptive systems by integrating learning and evolutionary search.
1. Multivariate analyses can examine responses that are jointly encoded in multiple voxels, unlike univariate analyses which look at individual voxels.
2. Multivariate approaches can utilize hidden quantities such as coupling strengths between neural signals that cannot be observed with univariate methods.
3. Classification models aim to predict a categorical variable from features, while regression predicts a continuous variable. Multivariate models consider responses across multiple voxels.
Possibility Theory versus Probability Theory in Fuzzy Measure TheoryIJERA Editor
The purpose of this paper is to compare probability theory with possibility theory, and to use this comparison in comparing probability theory with fuzzy set theory. The best way of comparing probabilistic and possibilistic conceptualizations of uncertainty is to examine the two theories from a broader perspective. Such a perspective is offered by evidence theory, within which probability theory and possibility theory are recognized as special branches. While the various characteristic of possibility theory within the broader framework of evidence theory are expounded in this paper, we need to introduce their probabilistic counterparts to facilitate our discussion.
Coreference Resolution using Hybrid Approachbutest
The document presents a hybrid approach for coreference resolution that uses different methods for pronouns and non-pronouns. For pronouns, it uses a salience-based technique to find antecedents within 2 sentences by evaluating syntactic and semantic constraints. For non-pronouns, it trains a classifier on coreference pairs to group markables into coreference classes. The approach is evaluated on a test set, achieving a precision of 66.1%, recall of 58.4% and an F1-measure of 62%.
Commonsense reasoning as a key feature for dynamic knowledge invention and co...Antonio Lieto
This document discusses commonsense reasoning and its importance for computational creativity and knowledge invention. It provides an overview of past AI and cognitive science approaches to commonsense reasoning such as semantic networks, frames, and default logic. It then presents the TCL (Typicality Description Logic) framework, which extends description logics with typicality, probabilities, and cognitive heuristics to model commonsense conceptual combination. The framework is applied to generate novel concepts to achieve goals and to dynamically classify multimedia content. Evaluations show it effectively reclassifies content and generates recommendations that users and experts find high quality.
This document discusses techniques for fast decision tree learning on microarray data. It introduces using attribute histograms to speed up the process of finding the best split points for decision tree learning. It also discusses optimizations for speeding up leave-one-out cross validation by reusing subtrees from previous runs. Experimental results on three microarray datasets show speedups of 150-400% from these techniques. Attribute pruning based on histogram indices is also introduced to further improve speed without loss of accuracy.
Mathematical foundations of consciousnessPronoy Sikdar
1. The document proposes mathematical foundations for consciousness based on set theory and non-well-founded sets. It employs Zermelo-Fränkel axioms and introduces the anti-foundation axiom to characterize sets and enable studying them from the outside.
2. Operators like the Russell operator R are introduced to distinguish between normal and abnormal sets. Axioms are proposed to characterize experience and consciousness as primitives and define "consciousness operators". The Russell operator R satisfies these axioms.
3. A process for labeling and decorating graphs is described, which induces virtual sets associated with graphs. This is applied to brain circuitry graphs via "histograms" to frame a theory of consciousness based
T4 Introduction to the modelling and verification of, and reasoning about mul...EASSS 2012
This document provides an overview of a course on modelling, verification, and reasoning in multi-agent systems. The course is divided into 6 lectures covering topics such as linear and branching time, cooperative agents, comparing semantics of alternating-time temporal logic, reasoning with examples, and complexity analysis of verification and reasoning problems. It also lists required background knowledge and recommended reading materials on related logics, automata theory, and specification and verification of multi-agent systems.
1. Logical Argument Mapping (LAM) is a method for building common ground through cognitive change using logical argument diagrams. It aims to make implicit assumptions and limitations explicit to promote reflection.
2. LAM uses valid argument schemes as a normative standard, challenging users to represent arguments fully and address objections. This process reveals gaps and drives users to continually improve understanding.
3. For cognitive change to occur, relevant information must be visible while reducing cognitive load. LAM aims to integrate with the World Wide Argument Web to allow sharing of arguments.
This document is Timothy Joseph Murphy's final paper for an epistemology seminar at the University of Cincinnati. It examines the concept of simplicity and its relationship to symmetry. Murphy argues that simplicity is a cognitive virtue realized through the application of symmetry in neural representation and processing. The paper discusses previous approaches to justifying simplicity, defines key concepts like symmetry, and proposes experiments to better understand how the brain represents and processes symmetry.
Are Evolutionary Algorithms Required to Solve Sudoku Problemscsandit
1. Evolutionary algorithms aim to iteratively improve solutions through random mutation and fitness evaluation, but can become trapped in local optima. The author developed a "greedy random" mutation approach that preferentially adds rather than removes values.
2. Experiments showed this greedy random mutation was sometimes more effective at solving harder Sudoku puzzles than traditional evolutionary algorithms. This implies the quality of random mutation can significantly impact evolutionary algorithm performance with Sudoku.
3. The greedy random mutation was integrated into the evolutionary algorithm lifecycle to balance exploration and exploitation. Candidates were assessed after a removal to concentrate entropy around boundary solutions.
The ecology of two theories: activity theory and distributed cognition in pra...Ruby Kuo
This document summarizes Halverson's analysis of the use of activity theory and distributed cognition theory in computer-supported cooperative work (CSCW) research. Halverson concluded that while both theories provide benefits, neither fully satisfies the needs of CSCW due to their complex frameworks and lack of predictive power. The author aims to continue Halverson's research by exploring the recent applications of these two theories in CSCW publications to develop an "ecological picture" of their similarities, differences, and relationships.
The document discusses various topics relating to knowledge representation in artificial intelligence, including:
1) Different types of knowledge that need representation including declarative, procedural, commonsense, and scientific knowledge.
2) Ontologies define terminology and objects/relationships in a systematic way to enable knowledge sharing between agents.
3) Semantic networks represent knowledge graphically with nodes for objects/events and arcs for relationships, enabling reasoning through inheritance and matching.
4) Conceptual graphs also represent knowledge graphically as a bipartite graph with concepts and relations, and can represent logical expressions.
The technology for building knowledge-based systems by inductive inference from examples has
been demonstrated successfully in several practical applications. This paper summarizes an approach to
synthesizing decision trees that has been used in a variety of systems, and it describes one such system,
ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal
with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is
discussed and two means of overcoming it are compared. The paper concludes with illustrations of current
research directions.
The document discusses machine learning and provides an overview of the field. It defines machine learning, explains why it is useful, and gives a brief tour of different machine learning techniques including decision trees, neural networks, Bayesian networks, and more. It also discusses some issues in machine learning like how learning problem characteristics and algorithms influence accuracy.
Applying Support Vector Learning to Stem Cells Classificationbutest
The document discusses applying machine learning algorithms like perceptrons and support vector machines to classify stem cell images. It describes imaging stem cell nuclei, the problem of stem cell classification, and online machine learning methods. The perceptron achieved 70.38% accuracy on test data while support vector machines with radial basis function kernels optimized by CMA-ES achieved 97.02% accuracy, showing machine learning is feasible for stem cell image classification.
This document provides an overview of the COMP3170 Artificial Intelligence and Machine Learning course at Hong Kong Baptist University. It includes information about the course lecturers and textbook, assessment details, a tentative schedule of topics and dates, and a brief description of the content to be covered each week.
Word accessible - .:: NIB | National Industries for the Blind ::.butest
The document provides an agenda and information for the 2009 NIB/NAEPB Annual Training Conference held in Kansas City, Missouri from October 21-24, 2009. The conference focused on technology and strategic impacts with sessions addressing business topics to help agency leaders and employees. Activities included keynote speakers, committee meetings, tours of Alphapointe Association for the Blind, and an awards banquet honoring employee award winners. Presentations and materials from the conference were made available online.
The document defines several key machine learning and neural network terminology including:
- Activation level - The output value of a neuron in an artificial neural network.
- Activation function - The function that determines the output value of a neuron based on its net input.
- Attributes - Properties of an instance that can be used to determine its classification in machine learning tasks.
- Axon - The output part of a biological neuron that transmits signals to other neurons.
Predicting performance in Recommender Systems - PosterAlejandro Bellogin
This document discusses predicting the performance of recommender systems. It proposes that data commonly available to recommender systems could enable estimating the success of recommendations. Specifically, it aims to 1) define a performance prediction theory for recommender systems, 2) adapt query performance techniques from information retrieval to recommendations, and 3) evaluate appropriate performance metrics. The research also explores applying these models when combining multiple recommendation strategies or hybrid recommender systems.
Heterogeneous Proxytypes as a Unifying Cognitive Framework for Conceptual Rep...Antonio Lieto
This document summarizes Antonio Lieto's work on developing a cognitive framework called heterogeneous proxytypes for conceptual representation and reasoning in artificial systems. The framework incorporates multiple knowledge representations, including prototypes, exemplars, and theories. It allows different representations and reasoning mechanisms to be activated based on context. Lieto describes cognitive models that integrate heterogeneous proxytypes, like the DUAL-PECCS system, and evaluates them on commonsense reasoning tasks.
Trajectory Data Fuzzy Modeling : Ambulances Management Use Caseijdms
Data captured through mobile devices and sensors represent valuable information for organizations. This
collected information comes in huge volume and usually carry uncertain data. Due to this quality issue
difficulties occur in analyzing the trajectory data warehouse. Moreover, the interpretation of the analysis
can vary depending on the background of the user and this will make it difficult to fulfill the analytical
needs of an enterprise. In this paper, we will show the benefits of fuzzy logic in solving the challenges
related to mobility data by integrating fuzzy concepts into the conceptual and the logical model. We use the
ambulance management use case to illustrate our contributions.
Methods of Combining Neural Networks and Genetic AlgorithmsESCOM
1. The document discusses methods for combining neural networks and genetic algorithms. It describes three main approaches: evolving connection weights, evolving network architectures, and evolving learning rules.
2. In the first approach, a genetic algorithm is used as the learning rule to optimize neural network weights. The second approach uses a genetic algorithm to select structural parameters of the network and trains the network separately. The third approach evolves parameters of the network's learning rule.
3. Combining neural networks and genetic algorithms can help compensate for each techniques' weaknesses in search, and may lead to highly successful adaptive systems by integrating learning and evolutionary search.
1. Multivariate analyses can examine responses that are jointly encoded in multiple voxels, unlike univariate analyses which look at individual voxels.
2. Multivariate approaches can utilize hidden quantities such as coupling strengths between neural signals that cannot be observed with univariate methods.
3. Classification models aim to predict a categorical variable from features, while regression predicts a continuous variable. Multivariate models consider responses across multiple voxels.
Possibility Theory versus Probability Theory in Fuzzy Measure TheoryIJERA Editor
The purpose of this paper is to compare probability theory with possibility theory, and to use this comparison in comparing probability theory with fuzzy set theory. The best way of comparing probabilistic and possibilistic conceptualizations of uncertainty is to examine the two theories from a broader perspective. Such a perspective is offered by evidence theory, within which probability theory and possibility theory are recognized as special branches. While the various characteristic of possibility theory within the broader framework of evidence theory are expounded in this paper, we need to introduce their probabilistic counterparts to facilitate our discussion.
Coreference Resolution using Hybrid Approachbutest
The document presents a hybrid approach for coreference resolution that uses different methods for pronouns and non-pronouns. For pronouns, it uses a salience-based technique to find antecedents within 2 sentences by evaluating syntactic and semantic constraints. For non-pronouns, it trains a classifier on coreference pairs to group markables into coreference classes. The approach is evaluated on a test set, achieving a precision of 66.1%, recall of 58.4% and an F1-measure of 62%.
Commonsense reasoning as a key feature for dynamic knowledge invention and co...Antonio Lieto
This document discusses commonsense reasoning and its importance for computational creativity and knowledge invention. It provides an overview of past AI and cognitive science approaches to commonsense reasoning such as semantic networks, frames, and default logic. It then presents the TCL (Typicality Description Logic) framework, which extends description logics with typicality, probabilities, and cognitive heuristics to model commonsense conceptual combination. The framework is applied to generate novel concepts to achieve goals and to dynamically classify multimedia content. Evaluations show it effectively reclassifies content and generates recommendations that users and experts find high quality.
This document discusses techniques for fast decision tree learning on microarray data. It introduces using attribute histograms to speed up the process of finding the best split points for decision tree learning. It also discusses optimizations for speeding up leave-one-out cross validation by reusing subtrees from previous runs. Experimental results on three microarray datasets show speedups of 150-400% from these techniques. Attribute pruning based on histogram indices is also introduced to further improve speed without loss of accuracy.
Mathematical foundations of consciousnessPronoy Sikdar
1. The document proposes mathematical foundations for consciousness based on set theory and non-well-founded sets. It employs Zermelo-Fränkel axioms and introduces the anti-foundation axiom to characterize sets and enable studying them from the outside.
2. Operators like the Russell operator R are introduced to distinguish between normal and abnormal sets. Axioms are proposed to characterize experience and consciousness as primitives and define "consciousness operators". The Russell operator R satisfies these axioms.
3. A process for labeling and decorating graphs is described, which induces virtual sets associated with graphs. This is applied to brain circuitry graphs via "histograms" to frame a theory of consciousness based
T4 Introduction to the modelling and verification of, and reasoning about mul...EASSS 2012
This document provides an overview of a course on modelling, verification, and reasoning in multi-agent systems. The course is divided into 6 lectures covering topics such as linear and branching time, cooperative agents, comparing semantics of alternating-time temporal logic, reasoning with examples, and complexity analysis of verification and reasoning problems. It also lists required background knowledge and recommended reading materials on related logics, automata theory, and specification and verification of multi-agent systems.
1. Logical Argument Mapping (LAM) is a method for building common ground through cognitive change using logical argument diagrams. It aims to make implicit assumptions and limitations explicit to promote reflection.
2. LAM uses valid argument schemes as a normative standard, challenging users to represent arguments fully and address objections. This process reveals gaps and drives users to continually improve understanding.
3. For cognitive change to occur, relevant information must be visible while reducing cognitive load. LAM aims to integrate with the World Wide Argument Web to allow sharing of arguments.
This document is Timothy Joseph Murphy's final paper for an epistemology seminar at the University of Cincinnati. It examines the concept of simplicity and its relationship to symmetry. Murphy argues that simplicity is a cognitive virtue realized through the application of symmetry in neural representation and processing. The paper discusses previous approaches to justifying simplicity, defines key concepts like symmetry, and proposes experiments to better understand how the brain represents and processes symmetry.
Are Evolutionary Algorithms Required to Solve Sudoku Problemscsandit
1. Evolutionary algorithms aim to iteratively improve solutions through random mutation and fitness evaluation, but can become trapped in local optima. The author developed a "greedy random" mutation approach that preferentially adds rather than removes values.
2. Experiments showed this greedy random mutation was sometimes more effective at solving harder Sudoku puzzles than traditional evolutionary algorithms. This implies the quality of random mutation can significantly impact evolutionary algorithm performance with Sudoku.
3. The greedy random mutation was integrated into the evolutionary algorithm lifecycle to balance exploration and exploitation. Candidates were assessed after a removal to concentrate entropy around boundary solutions.
The ecology of two theories: activity theory and distributed cognition in pra...Ruby Kuo
This document summarizes Halverson's analysis of the use of activity theory and distributed cognition theory in computer-supported cooperative work (CSCW) research. Halverson concluded that while both theories provide benefits, neither fully satisfies the needs of CSCW due to their complex frameworks and lack of predictive power. The author aims to continue Halverson's research by exploring the recent applications of these two theories in CSCW publications to develop an "ecological picture" of their similarities, differences, and relationships.
The document discusses various topics relating to knowledge representation in artificial intelligence, including:
1) Different types of knowledge that need representation including declarative, procedural, commonsense, and scientific knowledge.
2) Ontologies define terminology and objects/relationships in a systematic way to enable knowledge sharing between agents.
3) Semantic networks represent knowledge graphically with nodes for objects/events and arcs for relationships, enabling reasoning through inheritance and matching.
4) Conceptual graphs also represent knowledge graphically as a bipartite graph with concepts and relations, and can represent logical expressions.
The technology for building knowledge-based systems by inductive inference from examples has
been demonstrated successfully in several practical applications. This paper summarizes an approach to
synthesizing decision trees that has been used in a variety of systems, and it describes one such system,
ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal
with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is
discussed and two means of overcoming it are compared. The paper concludes with illustrations of current
research directions.
The document discusses machine learning and provides an overview of the field. It defines machine learning, explains why it is useful, and gives a brief tour of different machine learning techniques including decision trees, neural networks, Bayesian networks, and more. It also discusses some issues in machine learning like how learning problem characteristics and algorithms influence accuracy.
Applying Support Vector Learning to Stem Cells Classificationbutest
The document discusses applying machine learning algorithms like perceptrons and support vector machines to classify stem cell images. It describes imaging stem cell nuclei, the problem of stem cell classification, and online machine learning methods. The perceptron achieved 70.38% accuracy on test data while support vector machines with radial basis function kernels optimized by CMA-ES achieved 97.02% accuracy, showing machine learning is feasible for stem cell image classification.
This document provides an overview of the COMP3170 Artificial Intelligence and Machine Learning course at Hong Kong Baptist University. It includes information about the course lecturers and textbook, assessment details, a tentative schedule of topics and dates, and a brief description of the content to be covered each week.
Word accessible - .:: NIB | National Industries for the Blind ::.butest
The document provides an agenda and information for the 2009 NIB/NAEPB Annual Training Conference held in Kansas City, Missouri from October 21-24, 2009. The conference focused on technology and strategic impacts with sessions addressing business topics to help agency leaders and employees. Activities included keynote speakers, committee meetings, tours of Alphapointe Association for the Blind, and an awards banquet honoring employee award winners. Presentations and materials from the conference were made available online.
Rohana Rajapakse is a senior developer at GOSS Interactive Ltd in Plymouth, UK. He received his PhD in Computer Science from the University of Plymouth in 2004. His research interests include digital information management, text processing, and neural networks. He has published several journal articles and conference papers on topics such as adaptive information retrieval, document categorization, and computational linguistics.
The document summarizes the work done in WP2 "Indexing" of the SemanticHIFI project. WP2 developed algorithms and modules for extracting audio features to enable functionalities like music segmentation, rhythm description, tonality description, generic audio descriptor extraction, music remixing, browsing by lyrics, audio identification, and tempo/phase detection. The work resulted in executable modules and libraries that were integrated into applications developed in other work packages. Scientific research methodologies were employed and results were disseminated through publications, conferences, and demonstrations.
Improving the Performance of Action Prediction through ...butest
The document describes an approach to improve action prediction in smart homes by identifying abstract tasks from low-level inhabitant actions. The approach models actions as states in a simple Markov model. Actions are clustered into groups representing tasks, and hidden Markov models are created using the clusters as hidden states. On simulated data with embedded patterns, the approach achieved good prediction accuracy, but had only marginal performance on real home data which contained more noise. The identification of tasks is meant to provide context that can help predict the next action and task more accurately than using low-level actions alone.
This document details research applying genetic programming to optimize word aligners for machine translation. Genetic programming mimics biological evolution by applying selection, mutation, and crossover to programs to find better solutions. The researcher aims to evolve word alignment programs but has so far been unsuccessful due to issues with mutations generating invalid code. Implementation decisions around representation, selection, crossover, and halting criteria are discussed. Results show minor improvements but generations prove ineffective as crossover alone is not enough for variety and mutation is not working properly.
What s an Event ? How Ontologies and Linguistic Semantics ...butest
The document discusses challenges for machine learning models in extracting information about events from text. It describes different approaches to representing events, from single relations to complex structures with interconnected subevents. Proper representation requires modeling event granularity, ordering, interrelations and other factors. Learning the building blocks of events and how they connect poses difficult problems for machine learning systems.
Noshir Contractor - WebSci'09 - Society On-Linebutest
Web Science is well poised to make advances by facilitating collaboration between theories, data, methods, and computational infrastructure regarding social networks. Recent developments allow capturing relational metadata from social networks to better understand how communities emerge and are enabled. Exponential random graph modeling can detect structural patterns in large networks to test theories about motivations for link creation and dissolution. Understanding these generative mechanisms could help contextualize community goals.
1. The document discusses machine learning and provides an overview of key concepts like inductive reasoning, learning from examples, and the constituents of machine learning problems.
2. It explains that machine learning problems involve an example set, background concepts, background axioms, and potential errors in data. Common machine learning tasks are categorization and prediction.
3. The document also outlines the constituents of machine learning methods, including representation schemes, search methods, and approaches for selecting hypotheses when multiple solutions are produced.
This proposal seeks funding to develop technology using data mining and machine learning to detect insider threats and security breaches through email and application monitoring. Specifically, the proposal aims to:
1) Develop an email security appliance that integrates with mail servers and alerts of potential insider misuse while quarantining emails to mitigate damage.
2) Extend current email monitoring technology to also monitor applications handling document attachments to detect insider malfeasance.
3) Investigate additional non-email data sources like host-based sensors monitoring user actions and file activities to identify insider threats.
The proposal involves researchers at Columbia University and developers at System Detection, Inc. working to expand current email profiling technology and develop a proof-
This document provides background information on a management and visualization tool for text mining applications developed by Peishan Mao for her MSc project. It discusses natural language text classification and describes how suffix trees can be used to represent documents at a more granular level than traditional "bag-of-words" models. The document outlines the design of the tool, which aims to provide a flexible framework for text classification experiments and allow evaluation and refinement of classifiers. It also describes the implementation of the tool in C# with a Windows Forms interface and Access database.
The document discusses machine learning techniques for multivariate data analysis using the TMVA toolkit. It describes several common classification problems in high energy physics (HEP) and summarizes several machine learning algorithms implemented in TMVA for supervised learning, including rectangular cut optimization, likelihood methods, neural networks, boosted decision trees, support vector machines and rule ensembles. It also discusses challenges like nonlinear correlations between input variables and techniques for data preprocessing and decorrelation.
Motivated Machine Learning for Water Resource Managementbutest
The document discusses challenges in water resource management and the potential for embodied intelligence and motivated machine learning to help address these challenges. It proposes using a goal creation system in embodied intelligence to motivate a machine to learn how to efficiently interact with its environment. This approach could help integrate modeling and decision making to support sustainable water policies that consider various social, economic and environmental factors. The document outlines some key challenges in water management and argues that embodied intelligence trained with a goal creation mechanism may help overcome limitations of traditional machine learning models to better adapt to changing real-world environments.
The document discusses a novel domain ontology discovery method that exploits contextual information from knowledge sources to construct domain ontologies. It involves parsing text, identifying lexical patterns, extracting linguistic patterns, performing statistical token analysis using mutual information, and developing a taxonomy of domain concepts. The proposed method aims to assist in building domain ontologies more quickly and accurately compared to existing methods.
The document details and evaluates different technologies for gesture recognition, including computer vision, accelerometers, and gloves. It provides a literature review of papers on vision-based and accelerometer-based gesture recognition techniques. The document proposes parameters for evaluating and comparing these technologies, such as resolution, accuracy, latency, range of motion, user comfort, and cost. It assigns weights to these parameters based on the goals of developing a gesture recognition system for research purposes.
Knowledge mining is a process of extracting patterns and relationships from databases to improve decision-making. It has various applications including business intelligence, product design, manufacturing, and research profiling. The document then provides three examples: 1) A financial company used knowledge mining to analyze customer data and target marketing promotions. 2) Engineers have used it to help design products by matching requirements to existing part designs. 3) Researchers have used text mining to map relationships between topics in academic literature and identify active individuals and organizations.
The document provides an overview of machine learning and artificial intelligence concepts:
- Machine learning allows intelligent agents to autonomously discover knowledge from experience. It involves learning from examples without being explicitly programmed.
- Explanation-based learning systems generate explanations for training examples and generalize these to apply to new examples. Case-based learning directly applies previous cases to new problems.
- Connectionist models like neural networks are inspired by the brain and use interconnected nodes that are activated based on input signals to learn complex functions.
This document provides an introduction to machine learning, including representations of real-life phenomena as objects and features, supervised learning using labeled training data to learn functions, unsupervised learning to group unlabeled data into clusters, and examples of decision trees for word sense disambiguation and k-means clustering. Interactive demos and references are also listed.
A Survey Ondecision Tree Learning Algorithms for Knowledge DiscoveryIJERA Editor
Theimmense volumes of data are populated into repositories from various applications. In order to find out desired information and knowledge from large datasets, the data mining techniques are very much helpful. Classification is one of the knowledge discovery techniques. In Classification, Decision trees are very popular in research community due to simplicity and easy comprehensibility. This paper presentsan updated review of recent developments in the field of decision trees.
Introduction to Artificial IntelligenceLuca Bianchi
Artificial intelligence has been defined in many ways as our understanding has evolved. Currently, AI is divided into narrow, general and super intelligence based on capabilities. Machine learning is a key approach in AI and involves algorithms that can learn from data to improve performance. Deep learning uses neural networks with many layers to learn representations of data and has achieved success in areas like computer vision and natural language processing.
Multilevel techniques for the clustering problemcsandit
Data Mining is concerned with the discovery of interesting patterns and knowledge in data
repositories. Cluster Analysis which belongs to the core methods of data mining is the process
of discovering homogeneous groups called clusters. Given a data-set and some measure of
similarity between data objects, the goal in most clustering algorithms is maximizing both the
homogeneity within each cluster and the heterogeneity between different clusters. In this work,
two multilevel algorithms for the clustering problem are introduced. The multilevel
paradigm suggests looking at the clustering problem as a hierarchical optimization process
going through different levels evolving from a coarse grain to fine grain strategy. The clustering
problem is solved by first reducing the problem level by level to a coarser problem where an
initial clustering is computed. The clustering of the coarser problem is mapped back level-bylevel
to obtain a better clustering of the original problem by refining the intermediate different
clustering obtained at various levels. A benchmark using a number of data sets collected from a
variety of domains is used to compare the effectiveness of the hierarchical approach against its
single-level counterpart.
This document describes a new genetic algorithm (GA)-based system for predicting the future performance of individual stocks. The system uses GAs for inductive machine learning rather than optimization. It is compared to a neural network system using data from over 1,600 stocks. The study finds that the GA system can predict stock returns 12 weeks in the future and that combining GA and neural network forecasts provides synergistic benefits.
Improved Performance of Unsupervised Method by Renovated K-MeansIJASCSE
Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed K-Means algorithm is compared with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means (DWK-Means) algorithm by using Davis Bouldin index, Execution Time and Iteration count methods. Experimental results show that the proposed K-Means algorithm performed better on Iris and Wine dataset when compared with other three clustering methods.
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A New Active Learning Technique Using Furthest Nearest Neighbour Criterion fo...ijcsa
Active learning is a supervised learning method that is based on the idea that a machine learning algorithm can achieve greater accuracy with fewer labelled training images if it is allowed to choose the image from which it learns. Facial age classification is a technique to classify face images into one of the several predefined age groups. The proposed study applies an active learning approach to facial age classification which allows a classifier to select the data from which it learns. The classifier is initially trained using a small pool of labeled training images. This is achieved by using the bilateral two dimension linear discriminant analysis. Then the most informative unlabeled image is found out from the unlabeled pool using the furthest nearest neighbor criterion, labeled by the user and added to the
appropriate class in the training set. The incremental learning is performed using an incremental version of bilateral two dimension linear discriminant analysis. This active learning paradigm is proposed to be applied to the k nearest neighbor classifier and the support vector machine classifier and to compare the performance of these two classifiers.
Grounded theory is a qualitative research method that uses a systematic set of procedures to develop an inductively derived grounded theory about a phenomenon. The key aspects of grounded theory include:
1) Beginning analysis by making distinctions and categorizing data to develop dimensions, categories, and their relationships.
2) Creating codes to analyze categories and relationships and discovering connections through constant comparison across data.
3) Furthering analysis through iterative coding, memo-writing to refine categories and hypotheses, and theoretical sampling to collect more data to develop the emerging theory.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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A SURVEY ON OPTIMIZATION APPROACHES TO TEXT DOCUMENT CLUSTERINGijcsa
This document provides a survey of optimization approaches that have been applied to text document clustering. It discusses several clustering algorithms and categorizes them as partitioning methods, hierarchical methods, density-based methods, grid-based methods, model-based methods, frequent pattern-based clustering, and constraint-based clustering. It then describes several soft computing techniques that have been used as optimization approaches for text document clustering, including genetic algorithms, bees algorithms, particle swarm optimization, and ant colony optimization. These optimization techniques perform a global search to improve the quality and efficiency of document clustering algorithms.
Applying Machine Learning to Agricultural Databutest
This document discusses applying machine learning techniques to agricultural data. It describes a software tool called WEKA that allows experimenting with different machine learning algorithms on real-world datasets. As a case study, the document examines using machine learning to infer rules for culling less productive cows from dairy herd data. Several machine learning methods were tested on the data and produced encouraging results for using machine learning to help solve agricultural problems.
Nature-Inspired Mateheuristic Algorithms: Success and New Challenges Xin-She Yang
Nature-inspired metaheuristic algorithms mimic characteristics of natural systems to solve optimization problems. They have become popular due to their simplicity, ease of implementation, and ability to balance solution diversity and speed. However, there are still challenges in developing a theoretical framework and applying these algorithms to large-scale problems. Future work is needed to address these challenges and close the gap between theory and applications of metaheuristics.
Discover How Scientific Data is Used for the Public Good with Natural Languag...BaoTramDuong2
This document discusses using natural language processing techniques like n-grams, deep learning models, and named entity recognition to analyze scientific publications and identify references to datasets. It evaluates classifiers like recurrent neural networks and convolutional neural networks to perform sequence labeling and extract dataset citations. The goal is to help government agencies and researchers quickly find datasets, measures, and experts by automating the analysis of research articles.
Incremental learning from unbalanced data with concept class, concept drift a...IJDKP
Recently, stream data mining applications has drawn vital attention from several research communities.
Stream data is continuous form of data which is distinguished by its online nature. Traditionally, machine
learning area has been developing learning algorithms that have certain assumptions on underlying
distribution of data such as data should have predetermined distribution. Such constraints on the problem
domain lead the way for development of smart learning algorithms performance is theoretically verifiable.
Real-word situations are different than this restricted model. Applications usually suffers from problems
such as unbalanced data distribution. Additionally, data picked from non-stationary environments are also
usual in real world applications, resulting in the “concept drift” which is related with data stream
examples. These issues have been separately addressed by the researchers, also, it is observed that joint
problem of class imbalance and concept drift has got relatively little research. If the final objective of
clever machine learning techniques is to be able to address a broad spectrum of real world applications,
then the necessity for a universal framework for learning from and tailoring (adapting) to, environment
where drift in concepts may occur and unbalanced data distribution is present can be hardly exaggerated.
In this paper, we first present an overview of issues that are observed in stream data mining scenarios,
followed by a complete review of recent research in dealing with each of the issue.
Compex object, encapsulation,inheritancePooja Dixit
- Complex objects are built from simpler objects like integers, strings, Booleans using constructors like tuples, sets, lists, and arrays. Sets, tuples, and lists are especially important constructors.
- Encapsulation packages code and data together in an object. It distinguishes between interface and implementation and promotes modularity. Encapsulation is based on the concept of abstract data types.
- Inheritance allows a class to inherit attributes and methods from a superclass, extending and specializing the superclass. Identical parts are defined only once to promote reuse. Some systems support multiple inheritance. Inheritance can cause problems if attributes are redefined which inheritance resolution must address.
This document discusses different methods of cluster analysis. Cluster analysis is a statistical technique that groups similar objects together into clusters. There are several categories of clustering methods, including partitioning, hierarchical, density-based, grid-based, model-based, and constraint-based. The partitioning method divides data into a set number of partitions or clusters, while hierarchical methods create hierarchical groupings by either merging or dividing clusters. Density-based clustering focuses on grouping areas of high density, and grid-based clustering quantizes space into a grid for faster processing. Model-based clustering fits data to hypothesized models, and constraint-based clustering incorporates user-defined constraints.
This document discusses different methods of cluster analysis. Cluster analysis is a statistical technique that groups similar objects together into clusters. There are several categories of clustering methods, including partitioning, hierarchical, density-based, grid-based, model-based, and constraint-based. The partitioning method divides data into a set number of partitions or clusters, while hierarchical methods create hierarchical groupings by either merging or dividing clusters. Density-based clustering focuses on grouping areas of high density, and grid-based clustering quantizes space into a grid for faster processing. Model-based clustering fits data to hypothesized models, and constraint-based clustering incorporates user-defined constraints.
Hybrid Approach for Brain Tumour Detection in Image Segmentationijtsrd
In this paper we have considered illustrating a few techniques. But the numbers of techniques are so large they cannot be all addressed. Image segmentation forms the basics of pattern recognition and scene analysis problems. The segmentation techniques are numerous in number but the choice of one technique over the other depends only on the application or requirements of the problem that is being considered. Analysis of cluster is a descriptive assignment that perceive homogenous group of objects and it is also one of the fundamental analytical method in facts mining. The main idea of this is to present facts about brain tumour detection system and various data mining methods used in this system. This is focuses on scalable data systems, which include a set of tools and mechanisms to load, extract, and improve disparate data power to perform complex transformations and analysis will be measured between the way of measuring the Furrier and Wavelet Transform distance. Sandeep | Jyoti Kataria "Hybrid Approach for Brain Tumour Detection in Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33409.pdf Paper Url: https://www.ijtsrd.com/medicine/other/33409/hybrid-approach-for-brain-tumour-detection-in-image-segmentation/sandeep
The document discusses data mining and knowledge discovery in databases. It defines data mining as the nontrivial extraction of implicit and potentially useful information from large amounts of data. With huge increases in data collection and storage, data mining aims to analyze data and discover patterns that can provide insights and knowledge about businesses and the real world. The data mining process involves selecting, preprocessing, transforming, and analyzing data to extract hidden patterns and relationships, which are then interpreted and evaluated.
Building a Classifier Employing Prism Algorithm with Fuzzy LogicIJDKP
Classification in data mining is receiving immense interest in recent times. As the knowledge is based on
historical data, classifications of data are essential for discovering the knowledge. To decrease the
classification complexity, the quantitative attributes of data need splitting. But the splitting using the
classical logic is less accurate. This can be overcome by the use of fuzzy logic. This paper illustrates how to
build up the classification rules using the fuzzy logic. The fuzzy classifier is built on by using the prism
decision tree algorithm. This classifier produces more realistic results than the classical one. The
effectiveness of this method is justified over a sample dataset.
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los hábitos de consumo causado por las nuevas tecnologías. Describe cómo YouTube aprovecha la participación de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
1) They dressed Jackson in ornate costumes that conveyed images of purity, innocence, and humility.
2) Jackson was shown entering the courtroom as if on a red carpet, emphasizing his celebrity status.
3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
Social Networks: Twitter Facebook SL - Slide 1butest
The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
Hare Chevrolet is a car dealership located in Noblesville, Indiana that has successfully used social media platforms like Twitter, Facebook, and YouTube to create a positive brand image. They invest significant time interacting directly with customers online to foster a sense of community rather than overtly advertising. As a result, Hare Chevrolet has built a large, engaged audience on social media and serves as a model for how brands can use online presences strategically.
Welcome to the Dougherty County Public Library's Facebook and ...butest
This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
This document provides compatibility information for Olympus digital products used with Macintosh OS X. It lists various digital cameras, photo printers, voice recorders, and accessories along with their connection type and any notes on compatibility. Some products require booting into OS 9.1 for software compatibility or do not support devices that need a serial port. Drivers and software are available for download from Olympus and other websites for many products to enable use with OS X.
To use printers managed by the university's Information Technology Services (ITS), students and faculty must install the ITS Remote Printing software on their Mac OS X computer. This allows them to add network printers, log in with their ITS account credentials, and print documents while being charged per page to funds in their pre-paid ITS account. The document provides step-by-step instructions for installing the software, adding a network printer, and printing to that printer from any internet connection on or off campus. It also explains the pay-in-advance printing payment system and how to check printing charges.
The document provides an overview of the Mac OS X user interface for beginners, including descriptions of the desktop, login screen, desktop elements like the dock and hard disk, and how to perform common tasks like opening files and folders. It also addresses frequently asked questions for Windows users switching to Mac OS X, such as where documents are stored, how to save or find documents, and what the equivalent of the C: drive is in Mac OS X. The document concludes with sections on file management tasks like creating and deleting folders, organizing files within applications, using Spotlight search, and an overview of the Dashboard feature.
This document provides a checklist for securing Mac OS X version 10.5, focusing on hardening the operating system, securing user accounts and administrator accounts, enabling file encryption and permissions, implementing intrusion detection, and maintaining password security. It describes the Unix infrastructure and security framework that Mac OS X is built on, leveraging open source software and following the Common Data Security Architecture model. The checklist can be used to audit a system or harden it against security threats.
This document summarizes a course on web design that was piloted in the summer of 2003. The course was a 3 credit course that met 4 times a week for lectures and labs. It covered topics such as XHTML, CSS, JavaScript, Photoshop, and building a basic website. 18 students from various majors enrolled. Student and instructor evaluations found the course to be very successful overall, though some improvements were suggested like ensuring proper software and pairing programming/non-programming students. The document also discusses implications of incorporating web design material into existing computer science curriculums.
1. Berretty, P.M., Todd, P.M., and Blythe, P.W. (1997). Categorization by Elimination: A fast and frugal approach to categorization. In
M.G. Shafto and P. Langley (Eds.), Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society (pp. 43-48).
Mahwah, NJ: Lawrence Erlbaum Associates.
Categorization by Elimination: A Fast and Frugal Approach to Categorization
Patricia M. Berretty (berretty@mpipf-muenchen.mpg.de)
Peter M. Todd (ptodd@mpipf-muenchen.mpg.de)
Philip W. Blythe (blythe@mpipf-muenchen.mpg.de)
Center for Adaptive Behavior and Cognition
Max Planck Institute for Psychological Research
Leopoldstrasse 24, 80802 Munich Germany
the rabbit could also use several cues to make its category
assignment, as soon as it finds enough cues to decide
Abstract “predator”--for instance, that this bird is gliding--it will not
bother gathering any more information, and instead will
People and other animals are very adept at categorizing head for shelter. Obviously, in the case of the rabbit, speed
stimuli even when many features cannot be perceived. is of the essence when categorizing birds as predators or
Many psychological models of categorization, on the other
nonpredators. Humans face similar circumstances where
hand, assume that an entire set of features is known. We
present a new model of categorization, called Categorization rapid categorization is called for, making use of only
by Elimination, that uses as few features as possible to whatever information is immediately available. Being able
make an accurate category assignment. This algorithm to categorize rapidly the intention of another approaching
demonstrates that it is possible to have a categorization person as either hostile or courting, for instance, will
process that is fast and frugal--using fewer features than enable the proper reactions to ensure the most desirable
other categorization methods--yet still highly accurate in its outcome.
judgments. We show that Categorization by Elimination In this paper, we consider the case for a “fast and frugal”
does as well as human subjects on a multi-feature (à la Gigerenzer & Goldstein, 1996) model of
categorization task, judging intention from animate motion, categorization, akin to the lexicographic process of bird
and that it does as well as other categorization algorithms
identification described in the first paragraph. This model,
on data sets from machine learning. Specific predictions of
the Categorization by Elimination algorithm, such as the which we call Categorization by Elimination (CBE), uses
order of cue use during categorization and the time-course only as many of the available cues or features as are
of these decisions, still need to be tested against human necessary to first make a specific categorization. As a
performance. consequence, it often uses far fewer cues in categorizing a
given stimulus than do the standard cue-combination
models, yielding its fast frugality. This information-
1. Introduction processing advantage can be crucial in a variety of
categorization contexts where speed is called for, as in
Hiking through the Bavarian Alps, you come upon a identifying threats. On the other hand, the accuracy of this
large bird gliding over a meadow. You pull out your approach typically rivals that of more computationally
European bird guidebook to identify it. From the shape of extensive algorithms, as we will show. We therefore
its body, you assume that this is a bird of prey, so you turn propose Categorization by Elimination as a parsimonious
to the section on raptors in the guide. To determine the psychological model, as well as a potentially useful
exact species, you next use size to narrow down your candidate for applied machine-learning categorization
search to a few kinds of hawks; then you use color to tasks.
eliminate a couple more species; and finally with one last Categorization by Elimination is closely related to
cue--tail length--you can make a unique classification. Tversky’s Elimination by Aspects (EBA) model of choice
Using only four cues (or features), you correctly identify (Tversky, 1972). After describing competing
this bird as a sparrow hawk. You could take out your psychological and machine-learning models of
binoculars and check more cues to support this categorization in the next section, we discuss the
identification, but for a rapid decision these few cues are background of elimination models in section 3. We
enough. present the Categorization by Elimination model in section
How would this categorization process proceed if a 4. Most other recent models of human categorization
rabbit rather than a human were watching the bird? The focus on the use of two or three cues, situations in which
rabbit would not be interested in knowing the exact species CBE can show little advantage. Therefore, we have
of bird flying overhead, but rather would want to experimentally investigated a multiple-cue categorization
categorize it as predator or not, as quickly as possible--the task in which we can compare our model with others in
Rabbit’s Guide to Birds has only two short sections. While accounting for human performance with seven cue
2. dimensions. We describe this study, which involves usually use all of the cues that are present--that is, do not
categorizing animate motion trajectories into different discard any available information.
behavioral intentions, in section 5. CBE does as well as Exemplar models (Brooks, 1978; Estes, 1986; Medin &
linear categorization methods, and does not overfit the data Schaffer, 1978; Nosofsky, 1986) assume that when
as neural networks seem to. Next, in section 6 we look at presented with a novel object, humans compute the
how well our algorithm does alongside some of the similarity between that object and all the possible
multiple-attribute categorization methods developed in categories in which the novel object could be placed.
psychology and machine learning on standard data sets Similarity is a function of the sum of the distances between
from the latter field. This comparison shows that the object and all the exemplars in the particular category.
Categorization by Elimination can often compete in The object is placed into the category with which it is most
accuracy with more complex methods. Further, if similar.
minimizing the number of cues used is sought to maximize Nosofsky’s (1986) generalized context model (GCM)
computational speed, CBE usually emerges as the clear allows for variation in the amount of attention given to
winner. Finally in section 7 we consider some of the different features during categorization (see also Medin &
challenges still ahead, including how to test CBE against Schaffer, 1978). Therefore, it is possible that different
human learning data. cues will be used in different tasks. However, this
attention weight remains the same for the entire stimulus
2. Existing Categorization Models set for each particular categorization task, rather than
varying across different objects belonging to the same
Many different models of categorization have been category (in contrast to our new method, as we will see).
proposed in both the psychological and machine learning Decision Bound Theory (or DBT--see Ashby & Gott,
literature. Psychologists are primarily concerned with 1988) assumes that there is a multidimensional region
developing a model that best describes human associated with each category, and therefore that categories
categorization performance, while in machine learning the are separated by bounds. An object is categorized
goal is to develop an optimally-performing model--that is, according to the region of perceptual space in which it lies.
one with the highest accuracy of categorization. These two Similarly, neural network models (e.g., Kruschke, 1992)
goals are not necessarily mutually exclusive; indeed, one learn hyperplane boundaries between categories, capturing
of the main findings so far in the field of human this knowledge in their trainable weights. In both cases, all
categorization is that people are often able to achieve near of the cues available in a particular stimulus are used to
optimal performance (that is, categorize a stimulus set with determine the region of multidimensional space, and hence
minimal errors--see Ashby & Maddox, 1992). As a the associated category, in which that stimulus falls.
consequence, some models, including neural networks and These psychological models all categorize by integrating
SCA (Miller & Laird, 1996) are often aimed at filling both cues and using all the cues available (except in GCM if a
roles. cue has an attention weight of 0). In addition, training
However, the majority of psychological studies of these models to learn new categories is a relatively simple
categorization have used simple stimuli that vary on only a process. But the memory requirements assumed by these
few (2-4) dimensions, unlike the typical high-dimensional models do differ: for example, GCM assumes that all
machine learning applications. It remains to be seen exemplars ever encountered are stored and used when
whether humans can also be optimal at categorizing multi- categorizing a novel object, while DBT does not need to
dimensional objects. In addition, the predominant store any exemplars. In comparison, our CBE algorithm
psychological models of categorization have not addressed does not integrate all available cues, is similarly easy to
the issue of constraints, such as limited time and train, and typically requires little memory.
information. What might the categorization process be Another approach to psychological modeling is captured
when there are both time and information constraints, in the discrete symbol-processing framework of Miller and
either because there is an overwhelming number of Laird’s (1996) Symbolic Concept Acquisition (SCA)
possible cues to use or only a subset of cues available? model. Here rules are built up incrementally for
Here we briefly review some of the currently popular classifying stimuli according to specific features,
categorization models for human categorization and beginning with very general rules that test a single feature
machine learning with these questions in mind. and progressing to more detailed rules that must match the
Throughout the remainder of the paper we use the terms stimuli on many features. While there are similarities
cues, aspects, dimensions, and features, as appropriate, to between this approach and CBE (in particular, the order in
all mean roughly the same thing. which features are processed can be related to our cue
The predominant theories of categorization in the validity measure), one major difference is that new stimuli
psychology literature include exemplar models (Nosofsky, are first checked against rules using all available cues, and
1986), decision bound models (Ashby & Gott, 1988), and only when this fails are fewer cues tested against the more
neural network models (e.g. ALCOVE--see Kruschke, general rules. In contrast, CBE begins with a single cue,
1992). Each of these categorization models assumes that and only adds new ones if necessary, thereby minimizing
the stimuli may be represented as points in a computation. The earlier EPAM symbolic discrimination-
multidimensional space. Furthermore, these models all net model (Feigenbaum & Simon, 1984) tests rules in the
assume that humans integrate features--that is, combine
multiple cues to come to a final judgment--and that we
3. efficient general-to-specific method we advocate, but the important aspects). Possible remaining choices that do not
rest of our approach is distinct. possess the current aspect being used for evaluation (for
In machine learning, predominant categorization theories instance, restaurants that do not serve seafood) are
include neural networks, Classification and Regression eliminated from the choice set. Furthermore, only aspects
Trees (or CART--see Breiman, Friedman, Olshen, & that are present in the most recent choice set are considered
Stone, 1984), and decision trees (e.g., ID3--see Quinlan, (for instance, if all nearby seafood restaurants are cheap,
1993). The goal of these machine learning models is then expense will not be used as an aspect to distinguish
usually to maximize categorization accuracy on a given further among them). Additional aspects are used only
useful data set. Algorithm complexity and speed are not until a single choice can be made, which is different from
typically the most important factors in developing machine the categorization models described above that use all
learning models, so that many are not psychologically cues.
plausible.
One model that does attempt psychological plausibility
by applying selective attention to unsupervised concept 4. Categorization by Elimination
formation is Gennari’s (1991) CLASSIT. This system
Our new Categorization by Elimination algorithm is a
classifies objects initially using a subset of the available
noncompensatory lexicographic model of categorization, in
cues determined by their attentional salience. However, all
that it uses cues in a particular order, and categorization
cues must still be considered before a final decision is
decisions made by earlier cues cannot be altered (or
reached, due to a “worst case” stopping rule.
compensated for) by later cues. In CBE, cues are ordered
Thus, even though many of the machine learning models
and used according to their validity. For our present
(e.g., CART and CLASSIT) use only a few cues during a
purposes we define validity as a measure of how accurately
given categorization, the process of setting up the
a single cue categorizes some set of stimuli (i.e., percent
algorithm’s decision mechanisms beforehand, including
correct). This is calculated by running CBE only using the
determining which cues to use, can be very complex. In
single cue in question, and seeing how many correct
contrast, our CBE algorithm has a simple learning phase,
categorizations the algorithm is able to make. (If using the
and still maintains comparable accuracy using few cues.
single cue results in CBE being unable to decide between
multiple categories for a particular stimulus, as will often
3. Elimination Models be the case, the algorithm chooses one of those categories
Motivated by the concerns raised in section 1, we at random--in this case, cue validity will be related to a
wanted to develop a fast and frugal categorization method cue’s discriminatory power.) Thus if size alone is more
that combines the best aspects of both the psychological accurate in categorizing birds (or more successful at
and machine learning models. From the psychological narrowing down the possible categories) than shape alone,
models we used the concepts of simple training and size would have a higher cue validity than shape. (There
decision processes and a small memory load. From the are other ways that cues can be ordered besides by validity,
machine learning models we took the notion of such as randomly or in order of salience, which we are
categorizing stimuli without using all available cues. This currently exploring.)
combination led us to look into elimination models. CBE assumes that cue values are divided up into bins
Classical elimination models were conceived of for (either nominal or continuous) which correspond to certain
choice tasks (Restle, 1961; Tversky, 1972) In a sequential categories. These bins form the knowledge base that CBE
elimination choice model, an object is chosen by uses to map cue values onto the possible corresponding
repeatedly eliminating subsets of objects from further categories. As an example, the size cue dimension for
consideration, thereby whittling down the set of remaining birds could be divided into three bins: a large size bin
possibilities. First a particular subset of the original set is (which could be specified numerically, e.g. “over 50 cm”)
chosen with some probability, using a particular feature to corresponding to the categories of eagles, geese, and
determine the subset members. Subsequent subsets are swans; a medium size bin corresponding to crows, jays, and
chosen in the same manner, with successive features, until hawks; and a small size bin corresponding to sparrows and
only one object remains. finches.
The most widely known elimination model in To build up the appropriate bin structures, the relevant
psychology is Tversky’s (1972) Elimination by Aspects cue dimensions to use must be determined ahead of time.
(EBA) model of probabilistic choice. One of the At present we construct a complete bin structure before
motivating factors in developing EBA as a normative testing CBE’s categorization performance, but learning and
model of choice was that there are often many relevant testing could also be done incrementally. In either case,
cues that may be used in choosing among complex bins can be constructed in a variety of ways from the
alternatives (Tversky, 1972). Therefore, part of any training examples--in the next two sections, we present two
reasonable psychological model of choice should be a possibilities.
procedure to select and order the cues to use from among A flowchart of CBE is shown in Figure 1. Given a
many alternatives. In EBA, the cues, or aspects, to use are particular stimulus to categorize, an initial set of possible
selected according to their utility for some decision (for categories is assumed, along with the ordered list of cue
instance, to choose a restaurant from those nearby, what dimensions to be used. The categorization process begins
they serve and how much they charge might be the most by using the cue dimension C with the highest validity.
4. Next a subset S of the possible categories is created evidence for CBE to come from situations in which
containing just those categories that correspond to the first categorization may be affected by time and cue availability
cue C’s value for the current stimulus object (this subset is constraints. As mentioned in the introduction, one specific
determined through the binning map described earlier). If domain where time and number of available cues are
only one category corresponds to that cue value, the limited is in inferring intention from motion. Blythe,
categorization process ends with this single category. If Miller, and Todd (1996) conducted an experiment in which
more than one category corresponds to the current cue the subjects’ task was to infer intention from motion of two
value, that set of possible categories S is kept, and the cue animated bugs shown moving about on a computer screen.
with the next highest validity, C*, is checked. The set of The movement patterns had all been previously generated
categories S corresponding to the previous cue C’s value is by other subjects instructed to engage in various types of
intersected with the set, S*, of categories corresponding to interaction by each controlling the motion of a single on-
the present cue C*’s value. This is CBE’s elimination screen bug. The six possible categories of interactive
step. motion were: pursuit, evasion, fighting, courting, being
If only one category remains in the new set intersection, courted, and playing. For example, one subject’s task
the algorithm terminates at this point with that one would be to have their bug pursue the other bug, while the
category. If more than one category remains, this other subject would move their bug to evade their pursuing
intersection becomes the new set S of remaining opponent. Next, new subjects viewed the recorded bug
possibilities, the next cue is checked, and the process is interactions and through forced choice, categorized the
repeated. If the intersection is empty, then the present cue interactions as a specific type of intentional motion.
is ignored, the prior set S of categories is retained, and the Seven salient cues of motion were calculated for each of
next cue is evaluated. This process of checking cues and the recorded motion patterns (see Blythe, Miller, & Todd,
using them to reduce the remaining set of possible 1996, for details). These cues were used to compare
categories continues until a single category remains, or different categorization models with each other and against
until all the cues have been checked, in which case a human performance. (While we cannot be sure that these
category is chosen from the remaining set at random. are the exact cues used by the human subjects, it is a
This algorithm has several interesting features. It is reasonable set to start with.)
frugal in information, using only those cues necessary to The four models tested were CBE and three traditional
reach a decision. It is non-compensatory, with earlier cues cue-integrating compensatory algorithms: unit tallying
eliminating category-choice possibilities that can never be (counting up the total number of cues that indicate one
replaced by later cues. The binning functions used to category versus another, using the same bin mapping as
associate possible categories with particular cue values can CBE), weighted tallying (adding up weighted votes from
be as simple or detailed as desired, from one-parameter
median cuts to multiple-cutoff mappings. And the exact Get first cue C
order of cues used does not appear to be critical: in
preliminary tests, different random cue orderings vary the
algorithm’s categorization accuracy by only a few Set possible cat.s
percentage points (but, interestingly, the number of cues S to categories
used with different orderings does vary more widely). corresponding to
CBE is clearly similar to EBA in several aspects, though prev. cue C’s value
there are some important differences. First, EBA is a
probabilistic model of choice while CBE is (in its current
form) a deterministic model of categorization. Second, in Get next cue C*
CBE cues are ordered before categorizing so that the same
cue order is used to evaluate each object. In EBA, aspects
are selected probabilistically according to their weight. Set possible cat.s
Therefore, the order of aspects is not necessarily the same S* to categories
for each object. Third, as mentioned previously, EBA only corresponding to
chooses aspects that are present in the current set of current cue C*’s value
remaining possible choices, and therefore the process never
terminates with the empty set. However, to select such an
aspect, all candidates must be examined to determine Set S to
which aspects are still possible to use. CBE does no such if S contains intersection of if S is
checking ahead of time for appropriate cues to use, but more than 1 cat. S and S* empty
rather takes this circumstance into account in its behavior
when the intersection of current and previous possible if S contains
category sets comes up empty. 1 category
STOP
5. CBE and Human Data
Under what conditions might CBE be a plausible
Figure 1: Flow Diagram of CBE
description of human categorization? We expect the most
5. all the cues that indicate one category versus another, again and information-availability constraints are taken into
using CBE’s binning along with weights determined by account.
correlation), and a three-layer feed-forward neural network
model trained by backpropagation learning (see Gigerenzer 6. CBE and Machine Learning Algorithms
& Goldstein, 1996, for more details on the first two).
It is difficult to compare CBE to existing categorization
The bin structure used for CBE and the tallying
models on multiple-cue human data beyond the domain
algorithms were determined by considering the distribution
just presented, because few other experiments have been
of cue values for each category and placing the bin
performed with more than three or four cues. Instead, as
boundaries at points of minimum overlap between
an alternative test of CBE’s general accuracy potential, we
categories. As a result, some cue values could be mapped
examined how well CBE categorized various multi-
onto too few possible categories (e.g. if pursuit was usually
dimensional objects using data from the UCI Machine
fast and courtship usually slow, the fast velocity bin would
Learning Repository (Merz & Murphy, 1996). We
only map to pursuit, and thus would miss all those
compared the performance of CBE to a standard exemplar
instances
model and a three-layer feed-forward neural network
Method Cat Acc Avg
trained with backpropagation. Results are shown in Table
Cues
2 for categorization performance when trained on the full
Subject 49.33% ? data sets and generalization performance when trained on
CBE 65.33% 3.77 half of each data set and tested on the other half. In
Wtally 64% 7
addition, we include the best reported categorization
Utally 63.33% 7 performance we have found for each data set in the
Nnet 88.33% 7 machine learning literature.
For the following comparisons, CBE used “perfect”
Table 1: Categorization accuracies and average number of binnings for the cue values. This means that the cue-value
cues used for subjects and models (chance = 16.7%). bins always map to the entire set of possible categories
associated with those particular cue values (unlike the bins
of rapid, excited courtship motion). Thus this bin mapping in the motion categorization example, where only the most
made perfect categorization impossible for CBE in this prevalent categories in each bin were returned). With
domain, and yet it did surprisingly well. Table 1 lists the perfect binning, the same categorization accuracy is always
average categorization accuracies of the human subjects, obtained regardless of the order in which the cues are used.
the categorization accuracies for the four models, and the However, when the cues are ordered by validity,
average number of cues used (this value is unknown for categorizations can be accomplished using the fewest
the human subjects). Since there were six possible number of cues.
categories, chance performance is 16.7%. Table 2 shows the results of these comparisons for three
As can be seen in Table 1, human subjects performed data sets. The first is the famous iris flower database in
well above chance in this task, and the four categorization which there are 150 instances classified into three
algorithms performed better still. The neural network did categories (different iris species) using four continuous-
suspiciously far better than the human subjects, indicating valued features (lengths and widths of flower parts). The
that it has possibly been overtrained on this data. (When next comparison used wine recognition data, in which 13
tested on generalization ability on a further untrained set of chemical-content cues are used to classify 178 wines as
motion stimuli, the network’s performance drops to 68%, one of three particular Italian vintages. The third data set
while the other three algorithms hover around 56%.) The analyzed contains two mushroom categories, poisonous
tallying algorithms and CBE are all much closer to human and edible, with 22 nominally valued dimensions, and
performance, but CBE achieves its accuracy while using 8124 total instances.
only about half of the cues of the others. Overall, CBE does very well on these three sets of multi-
The difference in accuracy between subjects and the feature natural objects, while using only a small proportion
algorithms can be explained in part by the fact that the of the available cues. CBE even performs well in
algorithms are “trained” on all the stimuli, either through comparison with models specifically designed for the best
the binning process or neural network learning (300 motion possible performance on these particular data sets. We
patterns in this case). In contrast, subjects must make their were not expecting CBE to outperform these specialized
categorizations without previous exposure to these stimuli algorithms; merely being in the same ballpark is a
(under the assumption that they would already know the powerful testament to this approach’s potential accuracy
cue structures of these categories through their experiences across varied domains. Furthermore, these algorithms all
outside the lab). To make a more fine-grained assessment employ the full set of cues, making the contrast with
of how well each categorization algorithm matches the CBE’s high accuracy through limited information all the
human data, we are performing analyses of the case-by- more striking.
case categorizations made by subjects and algorithms. But
even without this detailed analysis, CBE emerges as a 7. Future Work
parsimonious contender among categorization algorithms
in this multi-cue domain, and the clear winner when time The results we have presented here indicate that a fast
and frugal approach to categorization is a viable alternative
6. to cue-integrating compensatory models. By only using knowledge, rivaling the abilities of much more complex
those cues necessary to first make a categorical decision, and sophisticated algorithms (not to mention human
CBE can categorize stimuli under time pressure and subjects!).
information constraints. Moreover, if certain cues are The following issues still need to be explored. First,
missing (i.e. some feature values are unknown or cannot be how should bin structures be created? Incremental
perceived), CBE can still use the other available cues to learning can build the cue-value bins gradually as more
come up with a category judgment (we are in process of and more stimuli are seen. But how far should this
collecting data on this type of generalization ability across learning process go, and in what way should it proceed?
different categorization algorithms). Yet CBE still We have presented two alternatives here, and there are
performs very accurately, despite its limited use of many others possible. One
Table 2: Categorization accuracies and average number of cues used for various models on three data sets
Model Train/Test Iris Wine Mushroom
Set Size
Cat Acc Avg Cues Cat Acc Avg Cues Cat Acc Avg Cues
CBE Full 91.33 % 40.00 % 96.63 % 20.74 % 91.71 % 26.11 %
Half 92.40 % 26.24 % 90.37 % 15.83 % 91.66 % 26.16 %
Nnet Full 97.67 % 100 % 100 % 100 % 86.21 % 100 %
Half 97.07 % 100 % 95.95 % 100 %
Best 98 % 100 % 95.00 %
Reported (James, 1985) (Aeberhard et al., 1992) (Schlimmer, 1987)
important issue to explore further is the performance stimuli. Journal of Experimental Psychology: Learning,
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requirements). rules in categorization: Contrasting novice and exper-
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categorizing multi-dimensional objects needs to be Psychology: Human Perception and Performance, 18,
collected and analyzed to compare CBE with other 50-71.
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intention from motion data that categorization accuracy simulation of adaptive behavior: Interactive studies of
varied little with changes in cue order can also be studied pursuit, evasion, courtship, fighting, and play.
experimentally. Proceedings of the Fourth International Conference on
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With these further explorations and extensions to CBE, Cognitive Psychology, 18, 500-549.
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