1. Feed-forward neural networks are composed of nodes connected in a directed graph without feedback loops. Information flows from input to output nodes through one or more hidden layers.
2. Each node receives weighted input signals, calculates a weighted sum, and applies an activation function to determine its output. During training, weights are adjusted to minimize error between network outputs and desired targets.
3. Self-organizing maps are neural networks that use unsupervised learning to produce a low-dimensional representation of input patterns. They cluster multidimensional data onto a two-dimensional map based on topological similarity.
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
Conditional random fields (CRFs) are probabilistic models for segmenting and labeling sequence data. CRFs address limitations of previous models like hidden Markov models (HMMs) and maximum entropy Markov models (MEMMs). CRFs allow incorporation of arbitrary, overlapping features of the observation sequence and label dependencies. Parameters are estimated to maximize the conditional log-likelihood using iterative scaling or tracking partial feature expectations. Experiments show CRFs outperform HMMs and MEMMs on synthetic and real-world tasks by addressing label bias problems and modeling dependencies beyond the previous label.
The document provides an overview of perceptrons and neural networks. It discusses how neural networks are modeled after the human brain and consist of interconnected artificial neurons. The key aspects covered include the McCulloch-Pitts neuron model, Rosenblatt's perceptron, different types of learning (supervised, unsupervised, reinforcement), the backpropagation algorithm, and applications of neural networks such as pattern recognition and machine translation.
Artificial Neural Network Lect4 : Single Layer Perceptron ClassifiersMohammed Bennamoun
This document provides an overview of single layer perceptrons (SLPs) and classification. It defines a perceptron as the simplest form of neural network consisting of adjustable weights and a bias. SLPs can perform binary classification of linearly separable patterns by adjusting weights during training. The document outlines limitations of SLPs, including their inability to represent non-linearly separable functions like XOR. It introduces Bayesian decision theory and how it can be used for optimal classification by comparing posterior probabilities given prior probabilities and likelihood functions. Decision boundaries are defined for dividing a feature space into non-overlapping regions to classify patterns.
MLPfit is a tool for designing and training multi-layer perceptrons (MLPs) for tasks like function approximation and classification. It implements stochastic minimization as well as more powerful methods like conjugate gradients and BFGS. MLPfit is designed to be simple, precise, fast and easy to use for both standalone and integrated applications. Documentation and source code are available online.
Neural networks can be biological models of the brain or artificial models created through software and hardware. The human brain consists of interconnected neurons that transmit signals through connections called synapses. Artificial neural networks aim to mimic this structure using simple processing units called nodes that are connected by weighted links. A feed-forward neural network passes information in one direction from input to output nodes through hidden layers. Backpropagation is a common supervised learning method that uses gradient descent to minimize error by calculating error terms and adjusting weights between layers in the network backwards from output to input. Neural networks have been applied successfully to problems like speech recognition, character recognition, and autonomous vehicle navigation.
This document discusses support vector machines (SVMs) for pattern classification. It begins with an introduction to SVMs, noting that they construct a hyperplane to maximize the margin of separation between positive and negative examples. It then covers finding the optimal hyperplane for linearly separable and nonseparable patterns, including allowing some errors in classification. The document discusses solving the optimization problem using quadratic programming and Lagrange multipliers. It also introduces the kernel trick for applying SVMs to non-linear decision boundaries using a kernel function to map data to a higher-dimensional feature space. Examples are provided of applying SVMs to the XOR problem and computer experiments classifying a double moon dataset.
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
Conditional random fields (CRFs) are probabilistic models for segmenting and labeling sequence data. CRFs address limitations of previous models like hidden Markov models (HMMs) and maximum entropy Markov models (MEMMs). CRFs allow incorporation of arbitrary, overlapping features of the observation sequence and label dependencies. Parameters are estimated to maximize the conditional log-likelihood using iterative scaling or tracking partial feature expectations. Experiments show CRFs outperform HMMs and MEMMs on synthetic and real-world tasks by addressing label bias problems and modeling dependencies beyond the previous label.
The document provides an overview of perceptrons and neural networks. It discusses how neural networks are modeled after the human brain and consist of interconnected artificial neurons. The key aspects covered include the McCulloch-Pitts neuron model, Rosenblatt's perceptron, different types of learning (supervised, unsupervised, reinforcement), the backpropagation algorithm, and applications of neural networks such as pattern recognition and machine translation.
Artificial Neural Network Lect4 : Single Layer Perceptron ClassifiersMohammed Bennamoun
This document provides an overview of single layer perceptrons (SLPs) and classification. It defines a perceptron as the simplest form of neural network consisting of adjustable weights and a bias. SLPs can perform binary classification of linearly separable patterns by adjusting weights during training. The document outlines limitations of SLPs, including their inability to represent non-linearly separable functions like XOR. It introduces Bayesian decision theory and how it can be used for optimal classification by comparing posterior probabilities given prior probabilities and likelihood functions. Decision boundaries are defined for dividing a feature space into non-overlapping regions to classify patterns.
MLPfit is a tool for designing and training multi-layer perceptrons (MLPs) for tasks like function approximation and classification. It implements stochastic minimization as well as more powerful methods like conjugate gradients and BFGS. MLPfit is designed to be simple, precise, fast and easy to use for both standalone and integrated applications. Documentation and source code are available online.
Neural networks can be biological models of the brain or artificial models created through software and hardware. The human brain consists of interconnected neurons that transmit signals through connections called synapses. Artificial neural networks aim to mimic this structure using simple processing units called nodes that are connected by weighted links. A feed-forward neural network passes information in one direction from input to output nodes through hidden layers. Backpropagation is a common supervised learning method that uses gradient descent to minimize error by calculating error terms and adjusting weights between layers in the network backwards from output to input. Neural networks have been applied successfully to problems like speech recognition, character recognition, and autonomous vehicle navigation.
This document discusses support vector machines (SVMs) for pattern classification. It begins with an introduction to SVMs, noting that they construct a hyperplane to maximize the margin of separation between positive and negative examples. It then covers finding the optimal hyperplane for linearly separable and nonseparable patterns, including allowing some errors in classification. The document discusses solving the optimization problem using quadratic programming and Lagrange multipliers. It also introduces the kernel trick for applying SVMs to non-linear decision boundaries using a kernel function to map data to a higher-dimensional feature space. Examples are provided of applying SVMs to the XOR problem and computer experiments classifying a double moon dataset.
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5.
---------------------------------
Read more about GA:
Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010.
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
This document discusses kernel methods and radial basis function (RBF) networks. It begins with an introduction and overview of Cover's theory of separability of patterns. It then revisits the XOR problem and shows how it can be solved using Gaussian hidden functions. The interpolation problem is explained and how RBF networks can perform strict interpolation through a set of training data points. Radial basis functions that satisfy Micchelli's theorem allowing for a nonsingular interpolation matrix are presented. Finally, the structure and training of RBF networks using k-means clustering and recursive least squares estimation is covered.
This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.
This document provides an overview of neural networks. It discusses how the human brain works and how artificial neural networks are modeled after the human brain. The key components of a neural network are neurons which are connected and can be trained. Neural networks can perform tasks like pattern recognition through a learning process that adjusts the connections between neurons. The document outlines different types of neural network architectures and training methods, such as backpropagation, to configure neural networks for specific applications.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
This document provides an overview of probabilistic graphical models. It discusses two types of probabilistic graphical models - Bayesian networks and Markov networks. Bayesian networks use directed graphs to represent conditional independence relationships between random variables. Markov networks use undirected graphs for the same purpose. The document outlines topics like representation, examples including naive Bayes classifiers and the Ising model, and inference and learning algorithms for probabilistic graphical models.
The document discusses artificial neural networks and classification using backpropagation, describing neural networks as sets of connected input and output units where each connection has an associated weight. It explains backpropagation as a neural network learning algorithm that trains networks by adjusting weights to correctly predict the class label of input data, and how multi-layer feed-forward neural networks can be used for classification by propagating inputs through hidden layers to generate outputs.
1. Machine learning involves developing algorithms that can learn from data and improve their performance over time without being explicitly programmed. 2. Neural networks are a type of machine learning algorithm inspired by the human brain that can perform both supervised and unsupervised learning tasks. 3. Supervised learning involves using labeled training data to infer a function that maps inputs to outputs, while unsupervised learning involves discovering hidden patterns in unlabeled data through techniques like clustering.
Activation functions and Training Algorithms for Deep Neural networkGayatri Khanvilkar
Training of Deep neural network is difficult task. Deep neural network train with the help of training algorithms and activation function This is an overview of Activation Function and Training Algorithms used for Deep Neural Network. It underlines a brief comparative study of activation function and training algorithms.
This document discusses part-of-speech (POS) tagging and different methods for POS tagging, including rule-based, stochastic, and transformation-based learning (TBL) approaches. It provides details on how rule-based tagging uses dictionaries and hand-coded rules, while stochastic taggers are data-driven and use hidden Markov models (HMMs) to assign the most probable tag sequences. TBL taggers start with an initial tag and then apply an ordered list of rewrite rules and contextual conditions to learn transformations that reduce tagging errors.
The document discusses various backtracking techniques including bounding functions, promising functions, and pruning to avoid exploring unnecessary paths. It provides examples of problems that can be solved using backtracking including n-queens, graph coloring, Hamiltonian circuits, sum-of-subsets, 0-1 knapsack. Search techniques for backtracking problems include depth-first search (DFS), breadth-first search (BFS), and best-first search combined with branch-and-bound pruning.
The document provides an overview of using Markov chains and recurrent neural networks (RNNs) for text generation. It discusses:
- How Markov chains can model text by treating sequences of words as "states" and predicting the next word based on conditional probabilities.
- The limitations of Markov chains for complex text generation.
- How RNNs address some limitations by incorporating memory via feedback connections, allowing them to better capture sequential relationships.
- Long short-term memory (LSTM) networks, which help combat the "vanishing gradient problem" to better learn long-term dependencies in sequences.
- How LSTMs can be implemented in Python using Keras to generate text character-by-character based on
word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.
BackTracking Algorithm: Technique and ExamplesFahim Ferdous
This slides gives a strong overview of backtracking algorithm. How it came and general approaches of the techniques. Also some well-known problem and solution of backtracking algorithm.
This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.
This presentation Neural Network will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a use case implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into these slides to understand how a neural network actually work.
Below topics are explained in this neural network presentation:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
Learn more at: https://www.simplilearn.com
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
This document introduces the brain and provides some facts about it, how to help the brain learn, details about the different parts of the brain, and how to believe in yourself. It encourages exploring your amazing brain and having fun while learning.
This document provides an introduction to neural networks, including their basic components and types. It discusses neurons, activation functions, different types of neural networks based on connection type, topology, and learning methods. It also covers applications of neural networks in areas like pattern recognition and control systems. Neural networks have advantages like the ability to learn from experience and handle incomplete information, but also disadvantages like the need for training and high processing times for large networks. In conclusion, neural networks can provide more human-like artificial intelligence by taking approximation and hard-coded reactions out of AI design, though they still require fine-tuning.
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5.
---------------------------------
Read more about GA:
Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010.
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
This document discusses kernel methods and radial basis function (RBF) networks. It begins with an introduction and overview of Cover's theory of separability of patterns. It then revisits the XOR problem and shows how it can be solved using Gaussian hidden functions. The interpolation problem is explained and how RBF networks can perform strict interpolation through a set of training data points. Radial basis functions that satisfy Micchelli's theorem allowing for a nonsingular interpolation matrix are presented. Finally, the structure and training of RBF networks using k-means clustering and recursive least squares estimation is covered.
This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.
This document provides an overview of neural networks. It discusses how the human brain works and how artificial neural networks are modeled after the human brain. The key components of a neural network are neurons which are connected and can be trained. Neural networks can perform tasks like pattern recognition through a learning process that adjusts the connections between neurons. The document outlines different types of neural network architectures and training methods, such as backpropagation, to configure neural networks for specific applications.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
This document provides an overview of probabilistic graphical models. It discusses two types of probabilistic graphical models - Bayesian networks and Markov networks. Bayesian networks use directed graphs to represent conditional independence relationships between random variables. Markov networks use undirected graphs for the same purpose. The document outlines topics like representation, examples including naive Bayes classifiers and the Ising model, and inference and learning algorithms for probabilistic graphical models.
The document discusses artificial neural networks and classification using backpropagation, describing neural networks as sets of connected input and output units where each connection has an associated weight. It explains backpropagation as a neural network learning algorithm that trains networks by adjusting weights to correctly predict the class label of input data, and how multi-layer feed-forward neural networks can be used for classification by propagating inputs through hidden layers to generate outputs.
1. Machine learning involves developing algorithms that can learn from data and improve their performance over time without being explicitly programmed. 2. Neural networks are a type of machine learning algorithm inspired by the human brain that can perform both supervised and unsupervised learning tasks. 3. Supervised learning involves using labeled training data to infer a function that maps inputs to outputs, while unsupervised learning involves discovering hidden patterns in unlabeled data through techniques like clustering.
Activation functions and Training Algorithms for Deep Neural networkGayatri Khanvilkar
Training of Deep neural network is difficult task. Deep neural network train with the help of training algorithms and activation function This is an overview of Activation Function and Training Algorithms used for Deep Neural Network. It underlines a brief comparative study of activation function and training algorithms.
This document discusses part-of-speech (POS) tagging and different methods for POS tagging, including rule-based, stochastic, and transformation-based learning (TBL) approaches. It provides details on how rule-based tagging uses dictionaries and hand-coded rules, while stochastic taggers are data-driven and use hidden Markov models (HMMs) to assign the most probable tag sequences. TBL taggers start with an initial tag and then apply an ordered list of rewrite rules and contextual conditions to learn transformations that reduce tagging errors.
The document discusses various backtracking techniques including bounding functions, promising functions, and pruning to avoid exploring unnecessary paths. It provides examples of problems that can be solved using backtracking including n-queens, graph coloring, Hamiltonian circuits, sum-of-subsets, 0-1 knapsack. Search techniques for backtracking problems include depth-first search (DFS), breadth-first search (BFS), and best-first search combined with branch-and-bound pruning.
The document provides an overview of using Markov chains and recurrent neural networks (RNNs) for text generation. It discusses:
- How Markov chains can model text by treating sequences of words as "states" and predicting the next word based on conditional probabilities.
- The limitations of Markov chains for complex text generation.
- How RNNs address some limitations by incorporating memory via feedback connections, allowing them to better capture sequential relationships.
- Long short-term memory (LSTM) networks, which help combat the "vanishing gradient problem" to better learn long-term dependencies in sequences.
- How LSTMs can be implemented in Python using Keras to generate text character-by-character based on
word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.
BackTracking Algorithm: Technique and ExamplesFahim Ferdous
This slides gives a strong overview of backtracking algorithm. How it came and general approaches of the techniques. Also some well-known problem and solution of backtracking algorithm.
This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.
This presentation Neural Network will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a use case implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into these slides to understand how a neural network actually work.
Below topics are explained in this neural network presentation:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
Learn more at: https://www.simplilearn.com
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
This document introduces the brain and provides some facts about it, how to help the brain learn, details about the different parts of the brain, and how to believe in yourself. It encourages exploring your amazing brain and having fun while learning.
This document provides an introduction to neural networks, including their basic components and types. It discusses neurons, activation functions, different types of neural networks based on connection type, topology, and learning methods. It also covers applications of neural networks in areas like pattern recognition and control systems. Neural networks have advantages like the ability to learn from experience and handle incomplete information, but also disadvantages like the need for training and high processing times for large networks. In conclusion, neural networks can provide more human-like artificial intelligence by taking approximation and hard-coded reactions out of AI design, though they still require fine-tuning.
This document discusses neural networks and their applications. It covers perceptrons, which are single-layer neural networks, and the perceptron training rule. It also describes gradient descent search and the delta rule for training neural networks. The document introduces multi-layer neural networks and the backpropagation algorithm for training these more complex networks. In the end, it provides examples of applications of neural networks such as text-to-speech, fraud detection, and game playing.
This document provides an overview of neural network analysis, including what neural networks are, their advantages and disadvantages, and two common types - multilayer perceptron and Kohonen networks. It describes how neural networks are trained using cases with input and output data, and how the network parameters are adjusted during training to best fit the data. The training process involves using a portion of cases for training, another portion for verification, and the remainder for testing the network.
This document discusses various data mining techniques, including artificial neural networks. It provides an overview of the knowledge discovery in databases process and the cross-industry standard process for data mining. It also describes techniques such as classification, clustering, regression, association rules, and neural networks. Specifically, it discusses how neural networks are inspired by biological neural networks and can be used to model complex relationships in data.
This document discusses how big data can provide value to businesses through volume, velocity, and variety of data. It explains that data can be turned into valuable insights and inputs that improve information, products/processes, and management decisions. Specifically, it outlines how large health data sources combined with analytics expertise can generate insights that improve clinical trials, health commissioning models, risk stratification, and pharmacovigilance.
The document discusses knowledge organization systems (KOS) and how the Simple Knowledge Organization System (SKOS) bridges KOS and the Semantic Web. It provides examples of KOS like taxonomies and thesauruses and explains how they are used differently than ontologies. SKOS is defined as an RDF vocabulary for representing KOS online in a machine-readable way and became a W3C standard in 2009.
This document discusses linked open data and how to publish data in a standardized, machine-readable format using semantic web technologies. It explains that linked data uses the Resource Description Framework (RDF) to represent information as a graph of interconnected resources identified by URIs. By publishing data according to linked data principles, separate databases can be connected to work as a single global database. The document provides examples of how cultural heritage data from different domains can be represented and linked in RDF, and outlines six steps for publishing linked data on the web.
This document discusses description logics, which are knowledge representation languages that can represent domain knowledge in a structured, logical way. Description logics were designed as an extension of frames and semantic networks to add formal logic-based semantics. Description logics use individuals, concepts, and roles to represent knowledge. Concepts can be combined using constructors like conjunction and existential restrictions. Description logics allow logical inference through concept subsumption, where more specific concepts are subsumed by more general ones.
Rajendra Akerkar discusses the difference between the current web which contains unstructured data in web documents versus the semantic web which aims to structure data to be readable by machines. Semantic markup languages like RDFa, microformats, and microdata embed structured data in web pages so they can be understood by both humans and machines. This brings the web of documents and web of data closer together. Search engines are now able to provide more relevant search results by utilizing structured data embedded in web pages.
This document provides an overview of statistical concepts relevant to data mining. It discusses data mining tools and techniques for revealing patterns in data. Key concepts covered include supervised vs unsupervised learning, classification vs regression, measures of central tendency, variance, standard deviation, hypothesis testing, and the normal distribution. Examples are provided to illustrate calculating averages, variance, hypothesis testing, and interpreting normal distributions. Exercises with solutions are included to demonstrate applying statistical concepts.
Can we see additional value in linking and exploiting big data for business and societal benefit?
If we bring together numerous data sources to provide a single reference point then we start to derive new value.
Until then, we simply risk creating new data silos.
The document discusses big data and analyzing large data sets. It provides definitions of big data as data that exceeds storage or compute capacity for timely decision making. It also discusses the diverse sources of data including user-generated content and server/sensor data. It then covers key aspects of big data including the immutable and time-based nature of data, technologies for storage, retrieval, processing and analysis of big data like Hadoop, MapReduce, and R, and properties of big data systems.
The document describes an intelligent natural language question answering system called ENLIGHT. It discusses what question answering is, how it relates to information retrieval and information extraction. It then covers the general approach taken by question answering systems, including question analysis, document retrieval and processing, answer extraction and construction. It also discusses techniques used by ENLIGHT like handling semantic symmetry, ambiguous modification and incorporating learning. ENLIGHT is shown to have better precision and faster response time compared to other systems.
Can You Really Make Best Use of Big Data?R A Akerkar
How big is big? What are the precise criteria for a data set to be considered big data? At least three major factors that contribute to the bigness of big data: Ubiquity and variety of data capturing devices for different types of information
Increase data resolution. Super-linear scaling of data production rate with data producers. Although big data has other dimensions too but these are not inherent to the "bigness" of big data.
Big Data and Harvesting Data from Social MediaR A Akerkar
The value of social media data is only as valuable as the information and insights we can extract from it. It is the information and insights that will help us make better decisions and give us a competitive edge.
The document discusses data mining and classification techniques. It defines data mining as the extraction of interesting patterns from large amounts of data. Classification involves using attributes of records in a training dataset to predict the class of new, unseen records. Decision trees are a common classification technique that use attributes to recursively split data into subgroups until each subgroup belongs to a single class. The document also discusses clustering, which organizes unlabeled data into groups without predefined classes.
This document provides an overview of link analysis and summarizes several common approaches:
1. Early approaches calculated site popularity based on incoming and outgoing links, but had limitations in accounting for link importance. HITS introduced the concepts of hubs and authorities, where authoritative sites receive links from important hubs.
2. PageRank assigns weight based on the rank of parent sites, inversely proportional to their outdegree. It models the probability of a random web surfer being on a page.
3. Link analysis approaches like HITS and PageRank have limitations, such as failing to account for dangling nodes with no links. Modifications include adding a small probability of randomly restarting from any page.
This document provides an overview of artificial neural networks. It discusses biological neurons and how they are modeled in computational systems. The McCulloch-Pitts neuron model is introduced as a basic model of artificial neurons that uses threshold logic. Network architectures including single and multi-layer feedforward and recurrent networks are described. Different learning processes for neural networks including supervised and unsupervised learning are summarized. The perceptron model is explained as a single-layer classifier. Multilayer perceptrons are introduced to address non-linear problems using backpropagation for supervised learning.
Binary classification and linear separators. Perceptron, ADALINE, artifical neurons. Artificial neural networks (ANNs), activation functions, and universal approximation theorem. Linear versus non-linear classification problems. Typical tasks, architectures and loss functions. Gradient descent and back-propagation. Support Vector Machines (SVMs), soft-margins and kernel trick. Connexions between ANNs and SVMs.
Artificial neural networks (ANNs) are inspired by biological neural networks such as the brain. ANNs contain artificial neurons that are connected in a similar way to biological neurons. The document discusses the basic components and properties of biological neurons, as well as models for artificial neurons and neural networks. It provides examples of how perceptrons can be used to learn simple functions through supervised learning algorithms that adjust the neuron weights.
Abstract: This PDSG workshop introduces basic concepts of the grandfather of neural networks - the Perceptron. Concepts covered are history, algorithm and limitations.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Machine Learning - Introduction to Neural NetworksAndrew Ferlitsch
Abstract: This PDSG workshop introduces basic concepts of neural networks. Concepts covered are Neurons, Binary vs. Categorical vs. Real Value output, activation functions, fully connected networks, deep neural networks, specialized learners, cost function and feed forward.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
My invited talk at the 2018 Annual Meeting of SIAM (Society of Industrial and...Anirbit Mukherjee
This is a slightly expanded version of the talk I gave at the 2018 ISMP (International Symposium on Mathematical Programming). This SIAM talk has some more introductory material than the ISMP talk.
This document discusses artificial neural networks and their components. It describes how a neural network consists of highly interconnected processing elements called neurons that can model the biological neuron system. The key components are the input layer, hidden layers, and output layer. There are different network architectures like single layer feedforward networks, multilayer feedforward networks, and recurrent networks. Learning in neural networks can be supervised, unsupervised, or reinforced.
My 2hr+ survey talk at the Vector Institute, on our deep learning theorems.Anirbit Mukherjee
This document provides an overview of several results from papers related to analyzing neural networks. It discusses questions about what functions neural networks can represent and the properties of their loss landscapes. Key results presented include showing neural networks can perform exact empirical risk minimization in polynomial time for 1D networks, proving networks can represent continuous piecewise linear functions, and demonstrating depth separations where shallower networks require much larger size to represent certain functions. Open problems are also discussed, such as fully characterizing the function space of neural networks.
This document describes an artificial neural network project presented by Rm.Sumanth, P.Ganga Bashkar, and Habeeb Khan to Madina Engineering College. It provides an overview of artificial neural networks and supervised learning techniques. Specifically, it discusses the biological structure of neurons and how artificial neural networks emulate this structure. It then describes the perceptron model and learning rule, and how multilayer feedforward networks using backpropagation can learn more complex patterns through multiple layers of neurons.
Artificial neural networks are computational models inspired by the human brain. They are composed of interconnected nodes that process information using a technique called machine learning. This report discusses the basic components of neural networks including neurons, layers, and training methods. It also provides examples of using neural networks to learn and implement simple logic functions like AND, OR, NAND, and NOR gates. The code shows how neural networks can be built and trained in MATLAB to recognize patterns in input data and produce the correct output.
Dr. kiani artificial neural network lecture 1Parinaz Faraji
The document provides a history of neural networks, beginning with McCulloch and Pitts creating the first neural network model in 1943. It then discusses several important developments in neural networks including perceptrons in the 1950s and 1960s, backpropagation in the 1980s, and neural networks being implemented in semiconductors in the late 1980s. The document also includes diagrams and explanations of biological neurons, artificial neurons, different types of activation functions, and key aspects of neural network architectures.
This summary provides an overview of the key points from the CS229 lecture notes document:
1. The document introduces neural networks and discusses representing simple neural networks as "stacks" of individual neuron units. It uses a housing price prediction example to illustrate this concept.
2. More complex neural networks can have multiple input features that are connected to hidden units, which may learn intermediate representations to predict the output.
3. Vectorization techniques are discussed to efficiently compute the outputs of all neurons in a layer simultaneously, without using slow for loops. Matrix operations allow representing the computations in a way that can leverage optimized linear algebra software.
The document provides an introduction to the perceptron model. It discusses how the perceptron was originally invented in 1958 as a machine for image recognition, with an array of photocells randomly connected to neurons. Weights were encoded using potentiometers, and weight updates during learning were performed by electric motors. It then discusses how multiple perceptrons can be combined to solve non-linearly separable problems like XOR. Finally, it provides details on perceptron weight calculation and the use of an activation function to produce the output in a nonlinear way similar to biological neurons.
JAISTサマースクール2016「脳を知るための理論」講義04 Neural Networks and Neuroscience hirokazutanaka
This document summarizes key concepts from a lecture on neural networks and neuroscience:
- Single-layer neural networks like perceptrons can only learn linearly separable patterns, while multi-layer networks can approximate any function. Backpropagation enables training multi-layer networks.
- Recurrent neural networks incorporate memory through recurrent connections between units. Backpropagation through time extends backpropagation to train recurrent networks.
- The cerebellum functions similarly to a perceptron for motor learning and control. Its feedforward circuitry from mossy fibers to Purkinje cells maps to the layers of a perceptron.
This document provides an overview of neural networks and related topics. It begins with an introduction to neural networks and discusses natural neural networks, early artificial neural networks, modeling neurons, and network design. It then covers multi-layer neural networks, perceptron networks, training, and advantages of neural networks. Additional topics include fuzzy logic, genetic algorithms, clustering, and adaptive neuro-fuzzy inference systems (ANFIS).
The document discusses character recognition using convolutional neural networks. It begins with an introduction to classifiers and gradient-based learning methods. It then describes how multiple perceptrons can be combined into a multilayer perceptron and trained using backpropagation. Next, it introduces convolutional neural networks, which offer improvements over multilayer perceptrons in performance, accuracy, and distortion invariance. It provides details on the topology and training of convolutional neural networks. Finally, it discusses the LeNet-5 convolutional neural network and its successful application to handwritten digit recognition.
Mathematical Foundation of Discrete time Hopfield NetworksAkhil Upadhyay
This document appears to be a seminar presentation about Hopfield networks. It introduces Hopfield networks as a form of recurrent neural network that can serve as associative memory and solve constraint satisfaction problems. It then discusses discrete Hopfield networks, which have binary neuron states and update asynchronously. The key properties and applications of discrete Hopfield networks are summarized, including how they can be used for content-addressable memory and determining if an input is a known or unknown pattern.
Multilayer Backpropagation Neural Networks for Implementation of Logic GatesIJCSES Journal
ANN is a computational model that is composed of several processing elements (neurons) that tries to solve a specific problem. Like the human brain, it provides the ability to learn
from experiences without being explicitly programmed. This article is based on the implementation of artificial neural networks for logic gates. At first, the 3 layers Artificial Neural Network is
designed with 2 input neurons, 2 hidden neurons & 1 output neuron. after that model is trained
by using a backpropagation algorithm until the model satisfies the predefined error criteria (e)
which set 0.01 in this experiment. The learning rate (α) used for this experiment was 0.01. The
NN model produces correct output at iteration (p)= 20000 for AND, NAND & NOR gate. For
OR & XOR the correct output is predicted at iteration (p)=15000 & 80000 respectively
The document describes a multilayer neural network presentation. It discusses key concepts of neural networks including their architecture, types of neural networks, and backpropagation. The key points are:
1) Neural networks are composed of interconnected processing units (neurons) that can learn relationships in data through training. They are inspired by biological neural systems.
2) Common network architectures include multilayer perceptrons and recurrent networks. Backpropagation is commonly used to train multilayer feedforward networks by propagating errors backwards.
3) Neural networks have advantages like the ability to model complex nonlinear relationships, adapt to new data, and extract patterns from imperfect data. They are well-suited for problems like classification.
This document contains lecture notes on sparse autoencoders. It begins with an introduction describing the limitations of supervised learning and the need for algorithms that can automatically learn feature representations from unlabeled data. The notes then state that sparse autoencoders are one approach to learn features from unlabeled data, and describe the organization of the rest of the notes. The notes will cover feedforward neural networks, backpropagation for supervised learning, autoencoders for unsupervised learning, and how sparse autoencoders are derived from these concepts.
Leveraging Big Data to Manage Transport Operations (LeMO) project will address these issues by investigating the implications of the utilisation of such big data to enhance the economic sustainability and competitiveness of European transport sector.
This document discusses the importance of connecting and exploiting big data through linking various data sources. It argues that simply creating new large data silos does not provide much value, and that true value is derived when numerous sources are brought together to create a single reference point. The document also discusses how linked data provides advantages over traditional big data approaches by structuring data around semantic triples that can interconnect different records and provide a bigger picture view. However, it cautions that linked data also requires rigorous controls and understanding to avoid simply creating another unstructured data source.
This document discusses semi-structured data extraction from web pages. It introduces semantic generators, which are sets of rules that translate HTML documents into XML. It describes the WebMantic architecture, which allows automatic generation of semantic generators and wrappers. A practical example of using WebMantic to extract data from a population website is provided. Experimental results on extracting data from several websites are also presented, along with conclusions and plans for future work.
The document discusses data mining and decision trees. It provides definitions of data mining as the extraction of patterns from large amounts of data. Decision trees are described as a way to generate classification rules from data through a tree structure. Each node in the tree represents an attribute, and leaves represent classifications. An example decision tree is provided to classify whether to play golf based on weather attributes. The accuracy of decision tree classifiers is evaluated based on how many test cases are correctly classified. Advantages of decision trees are that they can handle both numeric and categorical data and clearly show important attributes. Weaknesses include computational expense and some can only handle binary target classes. The ID3 algorithm is introduced as a method for building decision trees through information gain to
The document provides an overview of artificial intelligence and knowledge-based systems. It discusses definitions of intelligence and AI, as well as knowledge representation schemes like logical, procedural, semantic network, and frame-based representations. The key components of a knowledge-based system are described as the knowledge base, which represents problem domain knowledge, and the inference engine, which uses reasoning techniques to solve problems. Ideal features of knowledge-based systems include efficient problem-solving using knowledge, heuristics, and eliminating unproductive solutions.
This document discusses using case-based reasoning for diet menu planning. It provides an overview of related work using CBR for domains like psychiatry and Alzheimer's care. It then describes DietMaster, a knowledge-intensive CBR system for diet menu planning that uses domain knowledge from experts. DietMaster's architecture and functional components are explained, including its representation of knowledge through rules and cases. Finally, the structures for input cases, cases in process, and output/learned cases are defined.
Rational Unified Process for User Interface DesignR A Akerkar
The document discusses the Rational Unified Process (RUP) framework for user interface design. It begins by providing an overview of RUP and its goals of ensuring high-quality products are delivered on schedule and budget. It then discusses how RUP is analogous to making a movie, with many iterative artifacts and activities involved. The document also discusses how RUP differs from a traditional construction analogy, as software development involves more unpredictable elements. It provides examples of risk mitigation strategies used in RUP, such as iterative development, requirements management, component-based architecture, and visual modeling.
The document discusses the Unified Modeling Language (UML) and Rational Rose software. It explains that UML allows you to create models using diagrams like use case diagrams, sequence diagrams, and class diagrams. A use case diagram shows actors and use cases, and how the actors interact with the system. Sequence diagrams show object interactions and messages passed between objects. Class diagrams describe object types and their relationships. Rational Rose is a program that allows building these UML models.
This document discusses data preprocessing techniques for data mining. It explains that real-world data is often dirty, containing issues like missing values, noise, and inconsistencies. Major tasks in data preprocessing include data cleaning, integration, transformation, reduction, and discretization. Data cleaning techniques are especially important and involve filling in missing values, identifying and handling outliers, resolving inconsistencies, and reducing redundancy from data integration. Other techniques discussed include binning data for smoothing noisy values and handling missing data through various imputation methods.
The document discusses various aspects of software project management including project planning activities like estimation, scheduling, staffing, and risk handling. It describes different project organization structures like functional organization and project organization. It also discusses different team structures like chief programmer teams, democratic teams, and mixed teams. The document emphasizes the importance of careful project planning and producing a software project management plan document. It also discusses considerations for staffing a project team and attributes of a good software engineer.
Personalisation and Fuzzy Bayesian NetsR A Akerkar
The document discusses using techniques from artificial intelligence such as machine learning, inference, linguistics, and fuzzy logic to develop personalized services that create user profiles and prototypes based on a user's interests and needs to provide tailored information through various channels like web pages, computer interfaces, news, advertising, messages, and data mining. It also describes using Bayesian networks, decision trees, conceptual graphs, and other methods to construct theories of prototypes and information fusion to power these personalized services.
Agents are helpful, harmless, and honest. They are designed
to be helpful, harmless, and honest.
Ecological: Agents are adaptive and situated in an environment. They
evolve to fit their environment and other agents.
The views are not mutually exclusive. Most systems have elements of
all three views to varying degrees.
The choice depends on the application domain and design goals.
Multiagent Systems: R. Akerkar 18
The document discusses human-machine interface design. It defines key terms like HMI, MMI, CHI, HCI and describes the multi-disciplinary nature of interface design. It also outlines the user interface design process including task analysis, interface design activities, prototyping and evaluation. Usability principles are presented focusing on tasks, feedback, consistency and more. Encoding techniques and examples of good and bad interfaces are provided.
The document discusses description logics (DL), which are formal logic-based knowledge representation languages used to represent knowledge in terms of concepts, roles, and individuals. It covers the semantics of DL, basic tableau algorithms for reasoning, and more advanced tableau algorithms for more expressive DL languages. The key points are:
- DL allows knowledge to be represented through concepts, roles, and individuals. Tableau algorithms are commonly used for reasoning.
- The semantics of DL are defined using interpretations over a domain. Tableau algorithms work by trying to construct an interpretation that satisfies a concept.
- Basic tableau algorithms expand concept descriptions into a tableau using rules until a clash is found, proving unsatisfiability, or no
This document provides an overview of decision trees, including:
- Decision trees classify records by sorting them down the tree from root to leaf node, where each leaf represents a classification outcome.
- Trees are constructed top-down by selecting the most informative attribute to split on at each node, usually based on information gain.
- Trees can handle both numerical and categorical data and produce classification rules from paths in the tree.
- Examples of decision tree algorithms like ID3 that use information gain to select the best splitting attribute are described. The concepts of entropy and information gain are defined for selecting splits.
Building an Intelligent Web: Theory & PracticeR A Akerkar
The document discusses building an intelligent web through theory and practice of data mining and semantic web technologies. It provides an overview of key concepts like data transformation, relationship between precision and recall in information retrieval, representation of classes and instances in ontologies, using RDF and RDF Schema to represent metadata and schemas. Example code snippets and figures are included to illustrate concepts like transforming text to keywords, precision-recall graph, representing an ontology in RDFS.
Relationship between the Semantic Web and NLPR A Akerkar
The document discusses the relationship between natural language processing (NLP) and the semantic web. It argues that NLP technologies can help make the semantic web more accessible to humans by allowing them to interact with it using natural language, while the structured data and ontologies of the semantic web can enhance NLP systems by providing broader contextual knowledge. As examples, it describes how NLP annotations could be applied to semantic web resources and how a question answering system and personal data aggregation tool called Haystack have been developed to demonstrate natural language search over semantic web data.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
How to Setup Default Value for a Field in Odoo 17Celine George
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
How to Download & Install Module From the Odoo App Store in Odoo 17Celine George
Custom modules offer the flexibility to extend Odoo's capabilities, address unique requirements, and optimize workflows to align seamlessly with your organization's processes. By leveraging custom modules, businesses can unlock greater efficiency, productivity, and innovation, empowering them to stay competitive in today's dynamic market landscape. In this tutorial, we'll guide you step by step on how to easily download and install modules from the Odoo App Store.
Brand Guideline of Bashundhara A4 Paper - 2024khabri85
It outlines the basic identity elements such as symbol, logotype, colors, and typefaces. It provides examples of applying the identity to materials like letterhead, business cards, reports, folders, and websites.
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 8 - CẢ NĂM - FRIENDS PLUS - NĂM HỌC 2023-2024 (B...
Neural Networks
1. Feed-forward
Feed forward Neural Nets
& Self–Organising Maps
g g p
R. Akerkar
TMRF, Kolhapur, India
September-6-11 Data Mining - R. Akerkar 1
2. Feed–Forward Neural Networks
HISTORICAL BACKGROUND
1943 McCulloch and Pitts proposed the first
computational model of a neuron
1949 H bb proposed th fi t l
Hebb d the first learning rule
i l
1958 Rosenblatt’s work on perceptrons
1969 Minsky and Papert’s paper exposed limitations
Papert s
of the theory
1970s Decade of dormancy for neural networks
1980–90s Neural network return (self–organisation,
back–propagation algorithms, etc)
September-6-11 Data Mining - R. Akerkar 2
3. SOME FACTS
Human brain contains 1011 neurons
Each neuron is connected 104 others
Some scientists compared the brain with a
“complex, nonlinear, parallel computer”.
The l
Th largest modern neural networks achieve
t d l t k hi
the complexity comparable to a nervous
system of a fly
fly.
September-6-11 Data Mining - R. Akerkar 3
4. Neuron
The main purpose of
neurons is to receive,
analyse and transmit
further th i f
f th the information in
ti i
a form of signals (electric
pulses).
When neuron sends the
information we say that a
neuron “fires”.
September-6-11 Data Mining - R. Akerkar 4
5. EXCITATION AND INHIBITION
The receptors of a neuron are called synapses, and they are located
on many branches called dendrites. There are many types of
synapses, but roughly they can be divided into two classes:
Excitatory — a signal received at this synapse “encourages” the
encourages
neuron to fire.
Inhibitory – a signal received at this synapse will try to make the
neuron “ h t up”.
“shut ”
The neuron analyses all the signals received at its synapses. If most
of them are encouraging, then the neuron gets “excited” and fires its
encouraging excited
own message along a single wire called axon. The axon may have
branches to reach as many other neurons as possible.
September-6-11 Data Mining - R. Akerkar 5
6. A MODEL OF A SINGLE NEURON
(UNIT)
In 1943 McCulloch and Pitts proposed the following
idea:
Denote the incoming signals by x = (x1, x2, . . . , xn)
(the input),
and the output of a neuron by y (the output y = f(x)).
September-6-11 Data Mining - R. Akerkar 6
7. WEIGHTED INPUT
Synapses (receptors) of a neuron have weights
w = (w1,w2, . . . ,wn) which can have positive
w w ),
(excitatory) or negative (inhibitory) values. Each
incoming signal is multiplied by the weight of the
g g p y g
receiving synapse wixi. Then all the “weighted”
inputs are added together into a weighted sum v:
i=1 wixi = (w, x)
n
v = w1x 1 + w2x 2 + · · · + wnx n =
Example Let x = (0, 1, 1) and w = (1,−2, 4). Then
v=1·0−2·1+4·1=2
September-6-11 Data Mining - R. Akerkar 7
8. ACTIVATION (TRANSFER) FUNCTION
The output of a neuron y is decided by the activation function ϕ
(also transfer function), which uses the weighted sum v as the
argument:t
y = ϕ(v)
The most popular is a step function ( threshold function):
If the weighted sum v is large enough (e.g. v = 2 > 0), then the
neuron fires (y = ϕ(2) = 1).
September-6-11 Data Mining - R. Akerkar 8
10. FEED–FORWARD NEURAL
NETWORKS
A collection of neurons connected together in a network can be
represented by a directed graph:
Nodes and arrows represent neurons and links with the
direction of a signal flow between them. Each node has its
number and a link between two nodes will have a pair of
numbers (e.g. (1, 4) connecting nodes 1 and 4).
A neural network that does not contain cycles (feedback loops) is
called a feed–forward network (or perceptron).
September-6-11 Data Mining - R. Akerkar 10
11. INPUT AND OUTPUT NODES
Input nodes receive the signal directly from the environment (nodes
1, 2 and 3). They do not compute anything, but simply transfer the
input values.
Output nodes send the signal directly to the environment (nodes 4
and 5).
September-6-11 Data Mining - R. Akerkar 11
12. HIDDEN NODES AND LAYERS
A network may have hidden nodes — they are not connected
directly to the environment (“hidden” inside the network):
We may organise nodes in layers: input (1,2,3), hidden (4,5) and
output (6,7) layers. Some ff networks can h
t t (6 7) l S t k have several hidd
l hidden
layers.
September-6-11 Data Mining - R. Akerkar 12
13. WEIGHTS
Each jth node in a network has a set of weights wij . For example,
node 4 h a set of weights w4 = ( 14,w24,w34)
d has f i h (w ).
A network is defined if we know its topology (its graph), the set of
all weights wij and the transfer functions ϕ of all nodes.
September-6-11 Data Mining - R. Akerkar 13
14. Example
What will be the network output if the inputs are x1 = 1
and x2 = 0?
September-6-11 Data Mining - R. Akerkar 14
15. Answer
Calculate weighted sums in the first hidden layer:
v3 = w13x1 + w23x2 = 2 · 1 − 3 · 0 = 2
v4 = w14x1 + w24x2 = 1 · 1 + 4 · 0 = 1
Apply the transfer function:
y3 = ϕ(2) = 1, y4 = ϕ(1) = 1
Thus, the input to output layer (node 5) is (1, 1).
Now, calculate the weighted sum of node 5:
v5 = w35y3 + w45y4 = 2 · 1 − 1 · 1 = 1
The output is y5 = ϕ(1) = 1
September-6-11 Data Mining - R. Akerkar 15
16. TRAINING
Let us inverse the previous problem:
Suppose th t the inputs to the network are x1 = 1 and x2 = 0 and
S that th i t t th t k d 0, d
ϕ is a step function as in previous example. Find values of
weights wij such that the output of the network y5 = 0.
This problem is much more difficult, because it has infinite
number of solutions. The process of finding a set of weights such
that for a given input the network produces the desired output is
called training.
Algorithms for training neural networks can be supervised (with
a “teacher”) and unsupervised (self–organising)
September-6-11 Data Mining - R. Akerkar 16
17. SUPERVISED LEARNING
A set of pairs of inputs with their corresponding
desired outputs is called a training set. We may
think of a training set as a set of examples.
Supervised learning can be described by the
following
f ll i procedure:d
1. Initially set all the weights to some random values
y g
2. Feed the network with an input from one of the examples in
the training set
3. Compare the output of the network with the desired output
4. Correct the error by adjusting the weights of the nodes
5. Repeat from step 2 with another example from the training
set
September-6-11 Data Mining - R. Akerkar 17
18. Lab 12 (a)
Consider the unit shown in the figure. Suppose that the weights
corresponding to the three inputs have the following values:
w1 = 2
w2 = -4
W3 = 1
and the activation of the unit is given by the step function:
Calculate what will be the output value y of the unit for each of the
p
following input patterns:
September-6-11 Data Mining - R. Akerkar 18
19. Solution 12 (a)
To find the output value y for each p
p pattern we have to:
a) Calculate the weighted sum:
v = i wi xi = w1 x1 + w2 x2 + w3 x3
b) Apply the activation function to v
The calculations for each input pattern are:
September-6-11 Data Mining - R. Akerkar 19
23. Self–Organising Maps (SOM)
HISTORICAL BACKGROUND
1960s Vector quantisation p
q problems studied byy
mathematicians (Glienn, 1964; Stratonowitch, 1966).
1973 von der Malsburg did the first computer
simulation demonstrating self–organisation.
1976 Willshaw and von der Malsburg suggested the
idea of SOM
SOM.
1980s Kohonen further developed and studied
computational algorithms for SOM
SOM.
September-6-11 Data Mining - R. Akerkar 23
24. EUCLIDEAN SPACE
Points in Euclidean space have coordinates (e.g. x, y, z) presented
by real numbers R. We denote n–dimensional space by Rn.
Every point in Rn is defined by n coordinates:
yp y
{x1, . . . , xn}
or by an n–dimensional Vector
x = (x1, . . . , xn)
September-6-11 Data Mining - R. Akerkar 24
25. EXAMPLES
Example 1 In R1 (one–dimensional space or
(one dimensional
a line) points are represented by just one
number, such as a = (2) or b = (−1).
, ( ) ( )
Example 2 In R3 (three–dimensional space)
points are represented by three coordinates
x,
x y and z (or x1, x2 and x3) such as
),
a = (2,−1, 3).
September-6-11 Data Mining - R. Akerkar 25
26. EUCLIDEAN DISTANCE
Distance between two points a = (a1, . . . , an) and b =
p (
(b1, . . . , bn) in Euclidean space Rn is calculated as:
September-6-11 Data Mining - R. Akerkar 26
28. MULTIDIMENSIONAL DATA IN
BUSINESS
A bank gathered information about its customers:
g
We may consider each entry as a coordinate xi and
all the information about one customer as a point in
Rn (n–dimensional space).
How to analyse such data?
September-6-11 Data Mining - R. Akerkar 28
29. CLUSTERS
Multivariate analysis offers variety of methods to analyse
multidimensional data (e.g. NN). SOM is one of such techniques.
One f th
O of the main goals i t fi d clusters of points.
i l is to find l t f i t
Clusters are groups of points close to each other.
“Similar” customers would have small Euclidean distance between
them and would belong to the same g p (
g group (cluster).
)
September-6-11 Data Mining - R. Akerkar 29
30. SOM ARCHITECTURE
SOM uses neural networks without hidden layer and with
y
neurons in the output layer competing with each other,
so that only one neuron (the winner) can fire at a time.
September-6-11 Data Mining - R. Akerkar 30
31. SOM ARCHITECTURE (CONT.)
Input layer has n nodes. We can represent an input pattern by n–
dimensional vector x = ( 1, . . . , xn) ∈ Rn.
di i l t (x
Each neuron j on the output layer is connected to all input nodes, so
each neuron has n weights We represent them by n dimensional
weights. n–dimensional
vector wj = (w1j, . . . ,wnj) ∈ R n.
Usually neurons in the output layer are arranged in a line (
y p y g (one–
dimensional lattice) or in a plane (two–dimensional).
SOM uses unsupervised learning algorithm, which organises weights
j in h
wj i the output l i so that they “ i i ” the characteristics of the
lattice h h “mimic” h h i i f h
input patterns.
September-6-11 Data Mining - R. Akerkar 31
32. HOW DOES AN SOM WORK
The algorithm consists of three p
g processes: competition,
p ,
cooperation and adaptation.
Competition Input pattern x = (x1, . . . , xn) is compared
with th weight vector wj = ( 1j, . . . ,wnj) of every neuron
ith the i ht t (w f
in the output layer. The winner is the neuron whose
weight wj is the closest to the input x in terms of
Euclidean distance:
September-6-11 Data Mining - R. Akerkar 32
33. Example
Consider SOM with three inputs and two output nodes (A
and B) Let wA = (2 1 3) and wB = ( 2 0 1)
d B). L t (2,−1, d (−2, 0, 1).
Find which node wins if the input
x = (1 −2 2)
(1, 2,
Solution:
(−1 −2
What if x = (−1,−2, 0)?
September-6-11 Data Mining - R. Akerkar 33
34. Cooperation
The winner helps its neighbours in the output lattice.
Those nodes which are closer to the winner in the lattice get more
help, those which are further away get less.
If the winner is node i, then the amount of help to node j is
calculated using the neighbourhood function hij(dij), where dij is the
distance between i and j in the lattice. A good example of hij(d) is
g p ( )
Gaussian function:
Note that the winner also helps itself more than others (for dii = 0).
September-6-11 Data Mining - R. Akerkar 34
35. Adaptation
After the input x has been presented to SOM, the weights wj of
the nodes are adjusted so that they become “closer” to the input.
The exact formula for adaptation of weights is:
w’j = wj + αhij [x − wj ] ,
where α is the learning rate coefficient.
One can see that the amount of change depends on the
neighbourhood hij of the winner. So, the winner helps itself and
its neighbours to adapt.
Finally, the neighbourhood hij is also a function of time, such that
the neighbourhood shrinks with time (e.g. σ decreases with t).
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36. Example
Let us adapt the winning node from earlier Example
(w
( A = (2 1 3) f x = (1 2 2))
(2,−1, for (1,−2,
if α = 0.5 and h = 1:
September-6-11 Data Mining - R. Akerkar 36
37. TRAINING PROCEDURE
1.
1 Initially set all the weights to some random
values
2.
2 Feed a set of data into the network
3. Find the winner
4. Adjust the
4 Adj t th weight of th winner and it
i ht f the i d its
neighbours to be more like the input
5. Repeat f
5 R t from step 2 until th network
t til the t k
stabilises
September-6-11 Data Mining - R. Akerkar 37
38. APPLICATIONS OF SOM IN
BUSINESS
SOM can be very useful during the intelligence
y g g
phase of decision making. It helps to analyse and
understand rather complex and large amounts of
information (data)
(data).
Ability to visualise multi–dimensional data can be
used for presentations and reports.
Identifying clusters in the data (e.g. typical groups of
customers) can help optimise distribution of
resources (e g advertising products selection etc)
(e.g. advertising, selection, etc).
Can be used to identify credit–card fraud, errors in
data, etc.
September-6-11 Data Mining - R. Akerkar 38
39. USEFUL PROPERTIES OF SOM
Reducing dimensions (Indeed, SOM is a map
(Indeed
f : Rn → Zm)
Visualisation of clusters
Ordered display
Handles i i data
H dl missing d t
The learning algorithm is unsupervised.
September-6-11 Data Mining - R. Akerkar 39
40. Similarities and differences between feed-forward
neural networks and self-organising maps
l k d lf ii
Similarities are:
Both are feed-forward networks (no loops).
Nodes have weights corresponding to each
link.
Both networks require training.
September-6-11 Data Mining - R. Akerkar 40
41. The main differences are:
Self-organising maps (SOM) use just a single output layer, they do not have hidden
layers.
In feed-forward neural networks (FFNN) we have to calculate weighted sums of the
( ) g
nodes. There are no such calculations in SOM, weights are only compared with the
input patterns using Euclidean distance.
In FFNN the output values of nodes are important, and they are defined by the
p p , y y
activation functions. In SOM nodes do not have any activation functions, and the
output values are not important.
In FFNN all the output nodes can re, while in SOM only one.
p , y
The output of FFNN can be a complex pattern consisting of the values of all the
output nodes. In SOM we only need to know which of the output nodes is the
winner.
Training of FFNN usually employs supervised learning algorithms, which require a
training set. SOM use unsupervised learning algorithm.
There are, however, unsupervised training methods for FFNN as well.
September-6-11 Data Mining - R. Akerkar 41
42. Lab 13 (a)
Consider the self-organising map:
The output layer of this map consists of six nodes, A, B, C, D, E and F,
which are organised into a two-dimensional lattice with neighbours
connected by lines.
Each f the t t d h two inputs x1 and x2 ( t shown on the
E h of th output nodes has t i t d (not h th
diagram). Thus, each node has two weights corresponding to these inputs:
w1 and w2. The values of the weights for all output in the SOM nodes are
g
given in the table below:
Calculate which of the six output nodes is the winner if the input pattern is
p p p
x = (2, -4)?
September-6-11 Data Mining - R. Akerkar 42
43. Solution 13 (a)
First, we calculate the distance for each node:
,
The winner is the node with the smallest distance from x. Thus,
in this case the winner is node C (because 5 is the smallest
distance here).
September-6-11 Data Mining - R. Akerkar 43