WEKA: Practical Machine Learning Tools And Techniques
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This document discusses several machine learning techniques including decision trees, classification rules, support vector machines, multilayer perceptrons, clustering, and the EM algorithm. It provides examples and explanations of how each technique works, such as how decision trees deal with numeric attributes by evaluating information gain to determine split points, and how the EM algorithm iterates between expectation and maximization steps to estimate cluster probabilities and distribution parameters.
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This document discusses machine learning techniques for classification and clustering. It introduces case-based reasoning and k-nearest neighbors classification. It discusses how kernels can allow non-linear classification by mapping data to higher dimensions. It describes k-means clustering, which groups data by minimizing distances to cluster means. It also introduces agglomerative clustering, which successively merges the closest clusters.
Training Deep Networks with Backprop (D1L4 Insight@DCU Machine Learning Works...
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Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.eam2
The document discusses machine learning algorithms used to predict personal income from census data. Three algorithms were tested: neural networks, support vector machines, and maximum entropy modeling. Maximum entropy modeling achieved the best results at 87.32% accuracy by using a selection of important features and excluding less predictive features like the third attribute. Voting the results of the three algorithms produced an accuracy of 85.57%.
CSA 3702 machine learning module 2
This document provides an overview of decision tree algorithms for machine learning. It discusses key concepts such as:
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- The trees are built by splitting the data into purer subsets based on attribute tests, using measures like information gain.
- Issues like overfitting are addressed through techniques like reduced error pruning and rule post-pruning.
Adapted Branch-and-Bound Algorithm Using SVM With Model Selection
Branch-and-Bound algorithm is the basis for the majority of solving methods in mixed integer linear programming. It has been proving its efficiency in different fields. In fact, it creates little by little a tree of nodes by adopting two strategies. These strategies are variable selection strategy and node selection strategy. In our previous work, we experienced a methodology of learning branch-and-bound strategies using regression-based support vector machine twice. That methodology allowed firstly to exploit information from previous executions of Branch-and-Bound algorithm on other instances. Secondly, it created information channel between node selection strategy and variable branching strategy. And thirdly, it gave good results in term of running time comparing to standard Branch-and-Bound algorithm. In this work, we will focus on increasing SVM performance by using cross validation coupled with model selection.
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Random forest is an ensemble machine learning algorithm that combines multiple decision trees to improve predictive accuracy. It works by constructing many decision trees during training and outputting the class that is the mode of the classes of the individual trees. Random forest can be used for both classification and regression problems and provides high accuracy even with large datasets.
Chapter 17
The document describes greedy algorithms and how they work. It provides an example of an activity selection problem that can be solved using a greedy algorithm in O(n) time by always selecting the activity with the earliest finish time that does not conflict with previously selected activities. It also discusses how Huffman codes can be constructed greedily by always merging the two lowest frequency characters into a node, resulting in an optimal prefix coding tree.
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It gives a very detailed introduction about machine learning and decision tree construction.
Python code for decision tree classifier.
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1) The 1R algorithm generates a one-level decision tree by considering each attribute individually and creating branches for each attribute value. It assigns the majority class to each branch and chooses the attribute with the minimum error.
2) Naive Bayes classification assumes attributes are independent and calculates the probability of each class using Bayes' theorem. It handles missing and numeric attributes.
3) Decision tree algorithms like ID3 use a divide-and-conquer approach, selecting the attribute that maximizes information gain at each node to create branches. Gain ratio addresses issues with highly branched attributes.
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Terminology Machine Learning
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.
Techniques Machine Learning
Machine learning techniques can be used to enable computers to learn from data and perform tasks. Some key techniques discussed in the document include decision tree learning, artificial neural networks, Bayesian learning, support vector machines, genetic algorithms, graph-based learning, reinforcement learning, and pattern recognition. Each technique has its own strengths and applications.
Machine learning Introduction
Machine learning is the ability of machines to learn from experience and improve their performance on tasks over time without being explicitly programmed. It involves the development of algorithms that allow computers to learn from large amounts of data. There are different types of machine learning including supervised learning, unsupervised learning, and semi-supervised learning. The history of machine learning began in the 1950s with research into neural networks, pattern recognition, and knowledge systems. Significant developments occurred in each subsequent decade, including decision trees, connectionism, reinforcement learning, and support vector machines. Machine learning continues to progress and find new applications in areas like data mining, language processing, and robotics.
Areas of machine leanring
This document provides an overview of machine learning applications across several domains:
- Financial applications including trading strategies, forecasting, and portfolio management utilize techniques like reinforcement learning and neural networks.
- Weather forecasting uses neural networks, support vector machines, and time series analysis to predict temperature and rainfall.
- Speech recognition and natural language processing apply machine learning to tasks like document classification, tagging, and parsing using probabilistic and neural network models.
- Other applications include smart environments using predictive models, computer games using reinforcement learning, robotics combining mechanics and software, and medical decision support analyzing clinical and biological data.
AI: Planning and AI
A situated planning agent treats planning and acting as a single process rather than separate processes. It uses conditional planning to construct plans that account for possible contingencies by including sensing actions. The agent resolves any flaws in the conditional plan before executing actions when their conditions are met. When facing uncertainty, the agent must have preferences between outcomes to make decisions using utility theory and represent probabilities using a joint probability distribution over variables in the domain.
AI: Logic in AI 2
A simple planning agent uses percepts from the environment to build a model of the current state and calls a planning algorithm to generate a plan to achieve its goal. Practical planning involves restricting the language used to define problems, using specialized planners rather than general theorem provers, and adopting hierarchical decomposition to store and retrieve abstract plans from a library. A solution is a fully instantiated, totally ordered plan that guarantees achieving the goal.
AI: Logic in AI
The document discusses different types of logical reasoning systems used in artificial intelligence, including knowledge-based agents, first-order logic, higher-order logic, goal-based agents, knowledge engineering, and description logics. It provides examples of objects, properties, relations, and functions that can be represented and reasoned about logically. It also compares different approaches to logical indexing and outlines the key components and inference tasks involved in description logics.
AI: Learning in AI 2
Bayesian learning views hypotheses as intermediaries between data and predictions. Belief networks can represent learning problems with known or unknown structures and fully or partially observable variables. Belief networks use localized representations, whereas neural networks use distributed representations. Reinforcement learning uses rewards to learn successful agent functions, such as Q-learning which learns action-value functions. Active learning agents consider actions, outcomes, and how actions affect rewards received. Genetic algorithms evolve individuals to successful solutions measured by fitness functions. Explanation-based learning speeds up programs by reusing results of prior computations.
AI: Learning in AI
Neural networks can be used for machine learning tasks like classification. They consist of interconnected nodes that update their weight values during a training process using examples. Neural networks have been applied successfully to tasks like handwritten character recognition, autonomous vehicle control by observing human drivers, and text-to-speech pronunciation generation. Their architecture is inspired by the human brain but neural networks are trained using computational methods while the brain uses biological processes.
AI: Introduction to artificial intelligence
This document provides an introduction to artificial intelligence including definitions of AI, categories of AI systems, requirements for an artificially intelligent system, a brief history of AI, examples of AI in the real world, definitions of intelligent agents and different types of agent programs. It defines AI as the study of intelligent behavior in computational processes and how to make computers capable of tasks that humans currently perform better. It outlines categories of systems that think like humans rationally, or act like humans rationally. It also describes the requirements for a system to exhibit intelligent behavior through natural language processing, knowledge representation, reasoning, and machine learning.
AI: Belief Networks
Conditional planning deals with incomplete information by constructing conditional plans that account for possible contingencies. The agent includes sensing actions to determine which part of the plan to execute based on conditions. Belief networks are constructed by choosing relevant variables, ordering them, and adding nodes while satisfying conditional independence properties. Inference in multi-connected belief networks can use clustering, conditioning, or stochastic simulation methods. Knowledge engineering for probabilistic reasoning first decides on topics and variables, then encodes general and problem-specific dependencies and relationships to answer queries.
AI: AI & Searching
1. There are three main ways to avoid repeated states during search: do not return to the previous state, avoid paths with cycles, and do not re-generate any previously generated state.
2. Constraint satisfaction problems have additional structural properties beyond basic problem requirements, including satisfying constraints.
3. Best first search orders nodes so the best evaluation is expanded first, making it an informed search method.
AI: AI & Problem Solving
The document discusses various problem solving techniques in artificial intelligence, including different types of problems, components of well-defined problems, measuring problem solving performance, and different search strategies. It describes single-state and multiple-state problems, and defines the key components of a problem including the data type, operators, goal test, and path cost. It also explains different search strategies such as breadth-first search, uniform cost search, depth-first search, depth-limited search, iterative deepening search, and bidirectional search.
Data Mining: Text and web mining
This document discusses text and web mining. It defines text mining as analyzing huge amounts of text data to extract information. It discusses measures for text retrieval like precision and recall. It also covers text retrieval and indexing methods like inverted indices and signature files. Query processing techniques and ways to reduce dimensionality like latent semantic indexing are explained. The document also discusses challenges in mining the world wide web due to its size and dynamic nature. It defines web usage mining as collecting web access information to analyze paths to accessed web pages.
Data Mining: Outlier analysis
Outlier analysis is used to identify outliers, which are data objects that are inconsistent with the general behavior or model of the data. There are two main types of outlier detection - statistical distribution-based detection, which identifies outliers based on how far they are from the average statistical distribution, and distance-based detection, which finds outliers based on how far they are from other data objects. Outlier analysis is useful for tasks like fraud detection, where outliers may indicate fraudulent activity that is different from normal patterns in the data.
Data Mining: Mining stream time series and sequence data
This document discusses various methodologies for processing and analyzing stream data, time series data, and sequence data. It covers topics such as random sampling and sketches/synopses for stream data, data stream management systems, the Hoeffding tree and VFDT algorithms for stream data classification, concept-adapting algorithms, ensemble approaches, clustering of evolving data streams, time series databases, Markov chains for sequence analysis, and algorithms like the forward algorithm, Viterbi algorithm, and Baum-Welch algorithm for hidden Markov models.
Data Mining: Mining ,associations, and correlations
Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets that satisfy minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of
Data Mining: Graph mining and social network analysis
Graph mining analyzes structured data like social networks and the web through graph search algorithms. It aims to find frequent subgraphs using Apriori-based or pattern growth approaches. Social networks exhibit characteristics like densification and heavy-tailed degree distributions. Link mining analyzes heterogeneous, multi-relational social network data through tasks like link prediction and group detection, facing challenges of logical vs statistical dependencies and collective classification. Multi-relational data mining searches for patterns across multiple database tables, including multi-relational clustering that utilizes information across relations.
Data warehouse and olap technology
This document discusses data warehousing and online analytical processing (OLAP) technology. It defines a data warehouse, compares it to operational databases, and explains how OLAP systems organize and present data for analysis. The document also describes multidimensional data models, common OLAP operations, and the steps to design and construct a data warehouse. Finally, it discusses applications of data warehouses and efficient processing of OLAP queries.
Data Mining: Data processing
Data processing involves cleaning, integrating, transforming, reducing, and summarizing data from various sources into a coherent and useful format. It aims to handle issues like missing values, noise, inconsistencies, and volume to produce an accurate and compact representation of the original data without losing information. Some key techniques involved are data cleaning through binning, regression, and clustering to smooth or detect outliers; data integration to combine multiple sources; data transformation through smoothing, aggregation, generalization and normalization; and data reduction using cube aggregation, attribute selection, dimensionality reduction, and discretization.
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- 2. Decision Trees Dealing with numeric attributes Standard method: binary splits Steps to decide where to split: Evaluate info gain for every possible split point of attribute Choose “best” split point But this is computationally intensive
- 3. Decision Trees Example Split on temperature attribute: 64 65 68 69 70 71 72 72 75 75 80 81 83 85 Yes No Yes YesYes No No Yes YesYes No Yes Yes No temperature < 71.5: yes/4, no/2 temperature > 71.5: yes/5, no/3 Info([4,2],[5,3]) = 6/14 info([4,2]) + 8/14 info([5,3]) = 0.939 bits
- 4. Decision Trees Dealing with missing values: Split instances with missing values into pieces A piece going down a branch receives a weight proportional to the popularity of the branch weights sum to 1
- 5. Decision Trees Pruning Making the decision tree less complex by removing cases of over fitting We have two types of pruning: Prepruning: Trying to decide during tree building Postpruning: Doing pruning after the tree has been constructed The two types of postpruning thatare generally used are: Subtree replacement Subtree raising To decide whether to do postpruning or not, we calculate the error rate before and after the pruning
- 6. Decision Trees Subtree raising:
- 7. Decision Trees Subtree replacement
- 8. Classification rules Criteria for choosing tests: p/t ratio Maximizes the ratio of positive instances with stress on accuracy p[log(p/t) – log(p/t)] Maximizes the number of positive instances with lesser accuracy
- 9. Classification rules Generating good rules: We can remove over fitting by either pruning of trees during construction or after they have been fully constructed To prune during construction we check each newly added test. If the error rate on the pruning set increases because of this new test, we remove it
- 10. Classification rules Obtaining rules from partial decision trees: Algorithm
- 12. Classification rules As the node 4 was not replaced, we stop at this stage Now each leaf node gives us a possible rule Choose the leaf which covers the greatest number of instances
- 13. Extending linear models Support vector machines: Support vector machines are algorithms for learning linear classifier They use maximum marginal hyper plane: removes over fitting The instances closest to the maximum marginal hyper plane are support vectors, rest all instances can be ignored
- 15. Extending linear models Support vector machines: The hyper plane can be written as: Support vector: All instances for which alpha(i) > 0 b and alpha are determined using software packages The hyper plane can also be written using kernel as:
- 16. Extending linear models Multilayer perceptron: We can create a network of perceptron to approximate arbitrary target concepts Multilayer perceptron is an example of an artificial neural network Consists of: input layer, hidden layer(s), and output layer Structure of MLP is usually found by experimentation Parameters can be found using backpropagation
- 17. Extending linear models Examples:
- 18. Extending linear models Back propagation: f(x) = 1/(1+exp(-x)) Error = ½(y-f(x))^2 So we try to minimize the error and get: Now just calculate the above expression for all training instances and do: w(i) = w(i) – L(dE/dw) We assume values of w in the starting
- 19. Clustering Incremental clustering: Steps Tree consists of empty root node Add instances one by one Update tree at appropriately at each stage To update, find the right leaf for an instance May involve restructuring the tree Restructuring: Merging and Replacement Decisions are made using category utility
- 20. Clustering Example of incremental clustering:
- 21. EM Algorithm EM = ExpectationMaximization Generalize kmeans to probabilistic setting Iterative procedure: E “expectation” step: Calculate cluster probability for each instance M “maximization” step: Estimate distribution parameters from cluster probabilities Store cluster probabilities as instance weights Stop when improvement is negligible
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