Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Chapter 6. Mining Frequent Patterns, Associations and Correlations Basic Conc...Subrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Chapter 10. Cluster Analysis Basic Concepts and Methods.pptSubrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
The document discusses decision trees and random forest algorithms. It begins with an outline and defines the problem as determining target attribute values for new examples given a training data set. It then explains key requirements like discrete classes and sufficient data. The document goes on to describe the principles of decision trees, including entropy and information gain as criteria for splitting nodes. Random forests are introduced as consisting of multiple decision trees to help reduce variance. The summary concludes by noting out-of-bag error rate can estimate classification error as trees are added.
Random forests are an ensemble learning method that constructs multiple decision trees during training and outputs the class that is the mode of the classes of the individual trees. It improves upon decision trees by reducing variance. The algorithm works by:
1) Randomly sampling cases and variables to grow each tree.
2) Splitting nodes using the gini index or information gain on the randomly selected variables.
3) Growing each tree fully without pruning.
4) Aggregating the predictions of all trees using a majority vote. This reduces variance compared to a single decision tree.
The document discusses the random forest algorithm. It introduces random forest as a supervised classification algorithm that builds multiple decision trees and merges them to provide a more accurate and stable prediction. It then provides an example pseudocode that randomly selects features to calculate the best split points to build decision trees, repeating the process to create a forest of trees. The document notes key advantages of random forest are that it avoids overfitting and can be used for both classification and regression tasks.
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
Chapter 6. Mining Frequent Patterns, Associations and Correlations Basic Conc...Subrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Chapter 10. Cluster Analysis Basic Concepts and Methods.pptSubrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
The document discusses decision trees and random forest algorithms. It begins with an outline and defines the problem as determining target attribute values for new examples given a training data set. It then explains key requirements like discrete classes and sufficient data. The document goes on to describe the principles of decision trees, including entropy and information gain as criteria for splitting nodes. Random forests are introduced as consisting of multiple decision trees to help reduce variance. The summary concludes by noting out-of-bag error rate can estimate classification error as trees are added.
Random forests are an ensemble learning method that constructs multiple decision trees during training and outputs the class that is the mode of the classes of the individual trees. It improves upon decision trees by reducing variance. The algorithm works by:
1) Randomly sampling cases and variables to grow each tree.
2) Splitting nodes using the gini index or information gain on the randomly selected variables.
3) Growing each tree fully without pruning.
4) Aggregating the predictions of all trees using a majority vote. This reduces variance compared to a single decision tree.
The document discusses the random forest algorithm. It introduces random forest as a supervised classification algorithm that builds multiple decision trees and merges them to provide a more accurate and stable prediction. It then provides an example pseudocode that randomly selects features to calculate the best split points to build decision trees, repeating the process to create a forest of trees. The document notes key advantages of random forest are that it avoids overfitting and can be used for both classification and regression tasks.
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
The document discusses random forest, an ensemble classifier that uses multiple decision tree models. It describes how random forest works by growing trees using randomly selected subsets of features and samples, then combining the results. The key advantages are better accuracy compared to a single decision tree, and no need for parameter tuning. Random forest can be used for classification and regression tasks.
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document discusses Chapter 5 from the book "Data Mining: Concepts and Techniques" which covers frequent pattern mining, association rule mining, and correlation analysis. It provides an overview of basic concepts such as frequent patterns and association rules. It also describes efficient algorithms for mining frequent itemsets such as Apriori and FP-growth, and discusses challenges and improvements to frequent pattern mining.
Chapter - 6 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document describes Chapter 6 of the book "Data Mining: Concepts and Techniques" which covers the topics of classification and prediction. It defines classification and prediction and discusses key issues in classification such as data preparation, evaluating methods, and decision tree induction. Decision tree induction creates a tree model by recursively splitting the training data on attributes and their values to make predictions. The chapter also covers other classification methods like Bayesian classification, rule-based classification, and support vector machines. It describes the process of model construction from training data and then using the model to classify new, unlabeled data.
The document is about Edureka's Data Science Certification Training course. It covers the following key topics:
- An introduction to machine learning and how it works. Common machine learning techniques like supervised and unsupervised learning are discussed.
- Cluster analysis and k-means clustering are explained in detail as important unsupervised learning algorithms. K-means clustering partitions observations into k clusters where each observation belongs to the cluster with the nearest mean.
- A demo of k-means clustering is shown on a Netflix movie dataset to group movies based on characteristics and increase business. Testimonials from past learners praise the quality of Edureka's data science training.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
This document discusses support vector regression (SVR) for predicting salary data. It shows code in Python and R for loading and preparing a dataset, performing SVR with radial basis function (RBF) kernel, making predictions on new data, and plotting the results. Key steps include feature scaling the input and output variables, fitting an SVR regressor, transforming new inputs to the scaled space to make predictions, and plotting the original data points against the regression line.
The document discusses transfer learning and building complex models using Keras and TensorFlow. It provides examples of using the functional API to build models with multiple inputs and outputs. It also discusses reusing pretrained layers from models like ResNet, Xception, and VGG to perform transfer learning for new tasks with limited labeled data. Freezing pretrained layers initially and then training the entire model is recommended for transfer learning.
This document discusses various similarity measures that can be used to quantify the similarity between documents, queries, or a document and query in an information retrieval system. It describes classic measures like Dice coefficient, overlap coefficient, Jaccard coefficient, and cosine coefficient. It provides examples of calculating these measures and compares the relations between different measures. The document also discusses using term-document matrices and shows an example matrix.
Association analysis is a technique used to uncover relationships between items in transactional data. It involves finding frequent itemsets whose occurrence exceeds a minimum support threshold, and then generating association rules from these itemsets that satisfy minimum confidence. The Apriori algorithm is commonly used for this task, as it leverages the Apriori property to prune the search space - if an itemset is infrequent, its supersets cannot be frequent. It performs multiple database scans to iteratively grow frequent itemsets and extract high confidence rules.
Process of converting data set having vast dimensions into data set with lesser dimensions ensuring that it conveys similar information concisely.
Concept
R code
Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of data by transforming correlated variables into a smaller number of uncorrelated variables called principal components. The document discusses PCA concepts like projections, dimensionality reduction, and applications to housing data. It explains how PCA finds the directions of maximum variance in high-dimensional data and projects it onto a new coordinate system.
This document discusses rule-based classification. It describes how rule-based classification models use if-then rules to classify data. It covers extracting rules from decision trees and directly from training data. Key points include using sequential covering algorithms to iteratively learn rules that each cover positive examples of a class, and measuring rule quality based on both coverage and accuracy to determine the best rules.
Handling concept drift in data stream miningManuel Martín
The document summarizes Manuel Martín Salvador's master's thesis on handling concept drift in data streams. It discusses concept drift, online learning approaches, evaluation methods, and taxonomy of concept drift handling methods. The contributions include developing new change detection heuristics called MoreErrorsMoving, MaxMoving, and Moving Average (with two variants), as well as a hybrid approach combining the heuristics.
K-means clustering is an algorithm that groups data points into k number of clusters based on their similarity. It works by randomly selecting k data points as initial cluster centroids and then assigning each remaining point to the closest centroid. It then recalculates the centroids and reassigns points in an iterative process until centroids stabilize. While efficient, k-means clustering has weaknesses in that it requires specifying k, can get stuck in local optima, and is not suitable for non-convex shaped clusters or noisy data.
Introduction to Linear Discriminant AnalysisJaclyn Kokx
This document provides an introduction and overview of linear discriminant analysis (LDA). It discusses that LDA is a dimensionality reduction technique used to separate classes of data. The document outlines the 5 main steps to performing LDA: 1) calculating class means, 2) computing scatter matrices, 3) finding linear discriminants using eigenvalues/eigenvectors, 4) determining the transformation subspace, and 5) projecting the data onto the subspace. Examples using the Iris dataset are provided to illustrate how LDA works step-by-step to find projection directions that separate the classes.
The document discusses the K-nearest neighbors (KNN) algorithm, a simple machine learning algorithm used for classification problems. KNN works by finding the K training examples that are closest in distance to a new data point, and assigning the most common class among those K examples as the prediction for the new data point. The document covers how KNN calculates distances between data points, how to choose the K value, techniques for handling different data types, and the strengths and weaknesses of the KNN algorithm.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
K-means clustering is an algorithm that groups data points into k clusters based on their attributes and distances from initial cluster center points. It works by first randomly selecting k data points as initial centroids, then assigning all other points to the closest centroid and recalculating the centroids. This process repeats until the centroids are stable or a maximum number of iterations is reached. K-means clustering is widely used for machine learning applications like image segmentation and speech recognition due to its efficiency, but it is sensitive to initialization and assumes spherical clusters of similar size and density.
This chapter discusses advanced classification methods, including Bayesian belief networks, classification using backpropagation neural networks, support vector machines (SVM), and lazy learners. It describes Bayesian belief networks as probabilistic graphical models that represent conditional dependencies between variables. Backpropagation neural networks are introduced as a way to perform nonlinear regression to approximate functions through adjusting weights in a multi-layer feedforward network. SVM is covered as a method that transforms data into a higher dimensional space to find an optimal separating hyperplane, using support vectors.
Chapter 9 of the document discusses advanced classification methods including Bayesian belief networks, classification using backpropagation neural networks, support vector machines, classification with frequent patterns, lazy learning, and other techniques. It describes how these methods work, including how Bayesian networks are constructed, how backpropagation trains neural networks, how support vector machines find optimal separating hyperplanes, and considerations around efficiency and interpretability. The chapter also covers mathematical mappings of classification problems and discriminative versus generative classifiers.
The document discusses random forest, an ensemble classifier that uses multiple decision tree models. It describes how random forest works by growing trees using randomly selected subsets of features and samples, then combining the results. The key advantages are better accuracy compared to a single decision tree, and no need for parameter tuning. Random forest can be used for classification and regression tasks.
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document discusses Chapter 5 from the book "Data Mining: Concepts and Techniques" which covers frequent pattern mining, association rule mining, and correlation analysis. It provides an overview of basic concepts such as frequent patterns and association rules. It also describes efficient algorithms for mining frequent itemsets such as Apriori and FP-growth, and discusses challenges and improvements to frequent pattern mining.
Chapter - 6 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document describes Chapter 6 of the book "Data Mining: Concepts and Techniques" which covers the topics of classification and prediction. It defines classification and prediction and discusses key issues in classification such as data preparation, evaluating methods, and decision tree induction. Decision tree induction creates a tree model by recursively splitting the training data on attributes and their values to make predictions. The chapter also covers other classification methods like Bayesian classification, rule-based classification, and support vector machines. It describes the process of model construction from training data and then using the model to classify new, unlabeled data.
The document is about Edureka's Data Science Certification Training course. It covers the following key topics:
- An introduction to machine learning and how it works. Common machine learning techniques like supervised and unsupervised learning are discussed.
- Cluster analysis and k-means clustering are explained in detail as important unsupervised learning algorithms. K-means clustering partitions observations into k clusters where each observation belongs to the cluster with the nearest mean.
- A demo of k-means clustering is shown on a Netflix movie dataset to group movies based on characteristics and increase business. Testimonials from past learners praise the quality of Edureka's data science training.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
This document discusses support vector regression (SVR) for predicting salary data. It shows code in Python and R for loading and preparing a dataset, performing SVR with radial basis function (RBF) kernel, making predictions on new data, and plotting the results. Key steps include feature scaling the input and output variables, fitting an SVR regressor, transforming new inputs to the scaled space to make predictions, and plotting the original data points against the regression line.
The document discusses transfer learning and building complex models using Keras and TensorFlow. It provides examples of using the functional API to build models with multiple inputs and outputs. It also discusses reusing pretrained layers from models like ResNet, Xception, and VGG to perform transfer learning for new tasks with limited labeled data. Freezing pretrained layers initially and then training the entire model is recommended for transfer learning.
This document discusses various similarity measures that can be used to quantify the similarity between documents, queries, or a document and query in an information retrieval system. It describes classic measures like Dice coefficient, overlap coefficient, Jaccard coefficient, and cosine coefficient. It provides examples of calculating these measures and compares the relations between different measures. The document also discusses using term-document matrices and shows an example matrix.
Association analysis is a technique used to uncover relationships between items in transactional data. It involves finding frequent itemsets whose occurrence exceeds a minimum support threshold, and then generating association rules from these itemsets that satisfy minimum confidence. The Apriori algorithm is commonly used for this task, as it leverages the Apriori property to prune the search space - if an itemset is infrequent, its supersets cannot be frequent. It performs multiple database scans to iteratively grow frequent itemsets and extract high confidence rules.
Process of converting data set having vast dimensions into data set with lesser dimensions ensuring that it conveys similar information concisely.
Concept
R code
Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of data by transforming correlated variables into a smaller number of uncorrelated variables called principal components. The document discusses PCA concepts like projections, dimensionality reduction, and applications to housing data. It explains how PCA finds the directions of maximum variance in high-dimensional data and projects it onto a new coordinate system.
This document discusses rule-based classification. It describes how rule-based classification models use if-then rules to classify data. It covers extracting rules from decision trees and directly from training data. Key points include using sequential covering algorithms to iteratively learn rules that each cover positive examples of a class, and measuring rule quality based on both coverage and accuracy to determine the best rules.
Handling concept drift in data stream miningManuel Martín
The document summarizes Manuel Martín Salvador's master's thesis on handling concept drift in data streams. It discusses concept drift, online learning approaches, evaluation methods, and taxonomy of concept drift handling methods. The contributions include developing new change detection heuristics called MoreErrorsMoving, MaxMoving, and Moving Average (with two variants), as well as a hybrid approach combining the heuristics.
K-means clustering is an algorithm that groups data points into k number of clusters based on their similarity. It works by randomly selecting k data points as initial cluster centroids and then assigning each remaining point to the closest centroid. It then recalculates the centroids and reassigns points in an iterative process until centroids stabilize. While efficient, k-means clustering has weaknesses in that it requires specifying k, can get stuck in local optima, and is not suitable for non-convex shaped clusters or noisy data.
Introduction to Linear Discriminant AnalysisJaclyn Kokx
This document provides an introduction and overview of linear discriminant analysis (LDA). It discusses that LDA is a dimensionality reduction technique used to separate classes of data. The document outlines the 5 main steps to performing LDA: 1) calculating class means, 2) computing scatter matrices, 3) finding linear discriminants using eigenvalues/eigenvectors, 4) determining the transformation subspace, and 5) projecting the data onto the subspace. Examples using the Iris dataset are provided to illustrate how LDA works step-by-step to find projection directions that separate the classes.
The document discusses the K-nearest neighbors (KNN) algorithm, a simple machine learning algorithm used for classification problems. KNN works by finding the K training examples that are closest in distance to a new data point, and assigning the most common class among those K examples as the prediction for the new data point. The document covers how KNN calculates distances between data points, how to choose the K value, techniques for handling different data types, and the strengths and weaknesses of the KNN algorithm.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
K-means clustering is an algorithm that groups data points into k clusters based on their attributes and distances from initial cluster center points. It works by first randomly selecting k data points as initial centroids, then assigning all other points to the closest centroid and recalculating the centroids. This process repeats until the centroids are stable or a maximum number of iterations is reached. K-means clustering is widely used for machine learning applications like image segmentation and speech recognition due to its efficiency, but it is sensitive to initialization and assumes spherical clusters of similar size and density.
This chapter discusses advanced classification methods, including Bayesian belief networks, classification using backpropagation neural networks, support vector machines (SVM), and lazy learners. It describes Bayesian belief networks as probabilistic graphical models that represent conditional dependencies between variables. Backpropagation neural networks are introduced as a way to perform nonlinear regression to approximate functions through adjusting weights in a multi-layer feedforward network. SVM is covered as a method that transforms data into a higher dimensional space to find an optimal separating hyperplane, using support vectors.
Chapter 9 of the document discusses advanced classification methods including Bayesian belief networks, classification using backpropagation neural networks, support vector machines, classification with frequent patterns, lazy learning, and other techniques. It describes how these methods work, including how Bayesian networks are constructed, how backpropagation trains neural networks, how support vector machines find optimal separating hyperplanes, and considerations around efficiency and interpretability. The chapter also covers mathematical mappings of classification problems and discriminative versus generative classifiers.
Chapter 9 of the book discusses advanced classification methods including Bayesian belief networks, classification using backpropagation neural networks, support vector machines, frequent pattern-based classification, lazy learning, and other techniques. It describes how these methods work, including how to construct Bayesian networks, train neural networks using backpropagation, find optimal separating hyperplanes with support vector machines, and more. The chapter also covers topics like network topologies, training scenarios, efficiency and interpretability of different methods.
ANNs have been widely used in various domains for: Pattern recognition Funct...vijaym148
The document discusses artificial neural networks (ANNs), which are computational models inspired by the human brain. ANNs consist of interconnected nodes that mimic neurons in the brain. Knowledge is stored in the synaptic connections between neurons. ANNs can be used for pattern recognition, function approximation, and associative memory. Backpropagation is an important algorithm for training multilayer ANNs by adjusting the synaptic weights based on examples. ANNs have been applied to problems like image classification, speech recognition, and financial prediction.
This document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are inspired by biological neural networks and are composed of interconnected nodes that mimic neurons. ANNs use a learning process to update synaptic connection weights between nodes based on training data to perform tasks like pattern recognition. The document outlines the history of ANNs and covers popular applications. It also describes common ANN properties, architectures, and the backpropagation algorithm used for training multilayer networks.
The document discusses neural networks and their use for pattern detection and machine learning. It describes how neural networks are modeled after the human nervous system and consist of connected input/output units with associated weights. The key points covered include:
- Neural networks can build highly accurate predictive models by training on large datasets.
- Backpropagation is a common neural network learning algorithm that adjusts weights to predict the correct class label of inputs.
- Neural networks have strengths like tolerance to noisy data and ability to classify untrained patterns, but also weaknesses like long training times and lack of interpretability.
This document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are inspired by biological neural networks and are composed of interconnected nodes that mimic neurons. ANNs use a learning process to update synaptic connection weights between nodes based on training data to perform tasks like pattern recognition. The document outlines the history of ANNs and covers popular applications. It also describes common ANN properties, architectures, and the backpropagation algorithm used for training multilayer networks.
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.
This document provides an overview of knowledge representation techniques and object recognition. It discusses syntax and semantics in representation, as well as descriptions, features, grammars, languages, predicate logic, production rules, fuzzy logic, semantic nets, and frames. It then covers statistical and cluster-based pattern recognition methods, feedforward and backpropagation neural networks, unsupervised learning including Kohonen feature maps, and Hopfield neural networks. The goal is to represent knowledge in a way that enables object classification and decision-making.
This document provides an overview of knowledge representation techniques and object recognition. It discusses syntax and semantics in representation, as well as descriptions, features, grammars, languages, predicate logic, production rules, fuzzy logic, semantic nets, and frames. It then covers statistical and cluster-based pattern recognition methods, feedforward and backpropagation neural networks, unsupervised learning including Kohonen feature maps, and Hopfield neural networks. The goal is to represent knowledge in a way that enables object classification and decision-making.
Classifiers are algorithms that map input data to categories in order to build models for predicting unknown data. There are several types of classifiers that can be used including logistic regression, decision trees, random forests, support vector machines, Naive Bayes, and neural networks. Each uses different techniques such as splitting data, averaging predictions, or maximizing margins to classify data. The best classifier depends on the problem and achieving high accuracy, sensitivity, and specificity.
Classification by back propagation, multi layered feed forward neural network...bihira aggrey
Classification by Back Propagation, Multi-layered feed forward Neural Networks - Provides a basic introduction of classification in data mining with neural networks
This document provides an overview of neural networks and models. It discusses the basic structure and function of neurons, including their inputs, outputs, and voltage-gated ion channels. It then introduces some early and influential neural network models, including McCulloch-Pitts neurons, perceptrons, and Hopfield networks. The key aspects of these models are summarized, such as their attributes, computational abilities, learning rules, and limitations. Later developments like backpropagation and multilayer perceptrons are also outlined, which overcame limitations of earlier single-layer models.
(研究会輪読) Weight Uncertainty in Neural NetworksMasahiro Suzuki
Bayes by Backprop is a method for introducing weight uncertainty into neural networks using variational Bayesian learning. It represents each weight as a probability distribution rather than a fixed value. This allows the model to better assess uncertainty. The paper proposes Bayes by Backprop, which uses a simple approximate learning algorithm similar to backpropagation to learn the distributions over weights. Experiments show it achieves good results on classification, regression, and contextual bandit problems, outperforming standard regularization methods by capturing weight uncertainty.
The document discusses backpropagation, which is a popular neural network learning algorithm. It describes the key components of a neural network including the input, hidden, and output layers. During training, weights are adjusted to minimize error between the network's predictions and actual outputs. Backpropagation works by propagating error backwards from the output layer through hidden layers to update weights and biases using gradient descent. This helps the network learn and improve its ability to accurately predict the class labels of new input samples.
Artificial neural networks are computer programs that can recognize patterns in data and produce models to represent that data. They are inspired by the human brain in how knowledge is acquired through learning and stored in the connections between neurons. Neural networks learn by adjusting the strengths of connections between neurons based on examples provided during training. They are able to model and learn both linear and nonlinear relationships in data.
The document discusses text categorization and compares several machine learning algorithms for this task, including Support Vector Machines (SVM), Transductive SVM (TSVM), and SVM combined with K-Nearest Neighbors (SVM-KNN). It provides an overview of text categorization and challenges. It then describes SVM, TSVM which uses unlabeled data to improve classification, and SVM-KNN which combines SVM with KNN to better handle unlabeled data. Pseudocode is presented for the algorithms.
X-TREPAN: A MULTI CLASS REGRESSION AND ADAPTED EXTRACTION OF COMPREHENSIBLE D...cscpconf
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
The document describes an algorithm called X-TREPAN that extracts decision trees from trained neural networks. X-TREPAN is an enhancement of the TREPAN algorithm that allows it to handle both multi-class classification and multi-class regression problems. It can also analyze generalized feed forward networks. The algorithm was tested on several real-world datasets and was found to generate decision trees with good classification accuracy while also maintaining comprehensibility.
Similar to Chapter 9. Classification Advanced Methods.ppt (20)
Chapter 13. Trends and Research Frontiers in Data Mining.pptSubrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Chapter 4. Data Warehousing and On-Line Analytical Processing.pptSubrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Lecture 8a: Clustering Validity, Minimum Description Length (MDL), Introduction to Information Theory, Co-clustering using MDL. (ppt,pdf)
Deepayan Chakrabarti, Spiros Papadimitriou, Dharmendra Modha, Christos Faloutsos, Fully Automatic Cross-Associations, KDD 2004, Seattle, August 2004. [PDF]
Some details about MDL and Information Theory can be found in the book “Introduction to Data Mining” by Tan, Steinbach, Kumar (chapters 2,4).
This document discusses absorbing random walks on graphs. It explains that absorbing random walks can be used to compute the proximity of non-absorbing nodes to chosen absorbing nodes in a graph. The absorption probabilities provide a measure of how close each non-absorbing node is to the different absorbing nodes. These probabilities can be computed by iteratively updating the absorption probabilities of each non-absorbing node based on the probabilities of its neighbors. Absorbing random walks have applications in areas like information propagation, opinion formation, and semi-supervised learning.
Lecture 9: Dimensionality Reduction, Singular Value Decomposition (SVD), Principal Component Analysis (PCA). (ppt,pdf)
Appendices A, B from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Lecture 7: Hierarchical clustering, DBSCAN, Mixture models and the EM algorithm (ppt,pdf)
Chapter 8,9 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Lecture 10b: Classification. k-Nearest Neighbor classifier, Logistic Regression, Support Vector Machines (SVM), Naive Bayes (ppt,pdf)
Chapters 4,5 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Lecture 6: Min-wise independent hashing. Locality Sensitive Hashing. Clustering, K-means algorithm (ppt,pdf)
Chapter 3 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman.
Chapter 8 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Lecture 11: Naive Bayes classifier. Supervised Learning. Web Search and PageRank (ppt,pdf)
Chapter 5 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Chapter 13 from the book "Introduction to Information Retrieval" by C. Manning, P. Raghavan, H. Schutze
Lecture 4: Frequent Itemests, Association Rules. Evaluation. Beyond Apriori (ppt, pdf)
Chapter 6 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Chapter 6 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman.
Lecture 12: Link Analysis Ranking: PageRank, HITS, Random Walks (ppt,pdf)
Chapter 21 from the book "Introduction to Information Retrieval" by C. Manning, P. Raghavan, H. Schutze
Chapter 14 from the book "Networks, Crowds and Markets" by D. Easley, J. Kleinberg
Lecture 2: Data, pre-processing and post-processing
Chapters 2,3 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Chapter 1 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman
Lecture 5: Similarity and Distance. Metrics. Min-wise independent hashing. (ppt,pdf)
Chapter 3 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman.
Chapter 2 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
ACRP 4-09 Risk Assessment Method to Support Modification of Airfield Separat...
Chapter 9. Classification Advanced Methods.ppt
1. 1
Data Mining:
Concepts and Techniques
(3rd ed.)
Chapter 9
Classification: Advanced Methods
Subrata Kumer Paul
Assistant Professor, Dept. of CSE, BAUET
sksubrata96@gmail.com
2. 2
Chapter 9. Classification: Advanced Methods
Bayesian Belief Networks
Classification by Backpropagation
Support Vector Machines
Classification by Using Frequent Patterns
Lazy Learners (or Learning from Your Neighbors)
Other Classification Methods
Additional Topics Regarding Classification
Summary
3. 3
Bayesian Belief Networks
Bayesian belief networks (also known as Bayesian
networks, probabilistic networks): allow class
conditional independencies between subsets of variables
A (directed acyclic) graphical model of causal relationships
Represents dependency among the variables
Gives a specification of joint probability distribution
X Y
Z
P
Nodes: random variables
Links: dependency
X and Y are the parents of Z, and Y is
the parent of P
No dependency between Z and P
Has no loops/cycles
4. 4
Bayesian Belief Network: An Example
Family
History (FH)
LungCancer
(LC)
PositiveXRay
Smoker (S)
Emphysema
Dyspnea
LC
~LC
(FH, S) (FH, ~S) (~FH, S) (~FH, ~S)
0.8
0.2
0.5
0.5
0.7
0.3
0.1
0.9
Bayesian Belief Network
CPT: Conditional Probability Table
for variable LungCancer:
n
i
Y
Parents i
xi
P
x
x
P n
1
))
(
|
(
)
,...,
( 1
shows the conditional probability for
each possible combination of its parents
Derivation of the probability of a
particular combination of values of X,
from CPT:
5. 5
Training Bayesian Networks: Several
Scenarios
Scenario 1: Given both the network structure and all variables
observable: compute only the CPT entries
Scenario 2: Network structure known, some variables hidden: gradient
descent (greedy hill-climbing) method, i.e., search for a solution along
the steepest descent of a criterion function
Weights are initialized to random probability values
At each iteration, it moves towards what appears to be the best
solution at the moment, w.o. backtracking
Weights are updated at each iteration & converge to local optimum
Scenario 3: Network structure unknown, all variables observable:
search through the model space to reconstruct network topology
Scenario 4: Unknown structure, all hidden variables: No good
algorithms known for this purpose
D. Heckerman. A Tutorial on Learning with Bayesian Networks. In
Learning in Graphical Models, M. Jordan, ed.. MIT Press, 1999.
6. 6
Chapter 9. Classification: Advanced Methods
Bayesian Belief Networks
Classification by Backpropagation
Support Vector Machines
Classification by Using Frequent Patterns
Lazy Learners (or Learning from Your Neighbors)
Other Classification Methods
Additional Topics Regarding Classification
Summary
7. 7
Classification by Backpropagation
Backpropagation: A neural network learning algorithm
Started by psychologists and neurobiologists to develop
and test computational analogues of neurons
A neural network: A set of connected input/output units
where each connection has a weight associated with it
During the learning phase, the network learns by
adjusting the weights so as to be able to predict the
correct class label of the input tuples
Also referred to as connectionist learning due to the
connections between units
8. 8
Neural Network as a Classifier
Weakness
Long training time
Require a number of parameters typically best determined
empirically, e.g., the network topology or “structure.”
Poor interpretability: Difficult to interpret the symbolic meaning
behind the learned weights and of “hidden units” in the network
Strength
High tolerance to noisy data
Ability to classify untrained patterns
Well-suited for continuous-valued inputs and outputs
Successful on an array of real-world data, e.g., hand-written letters
Algorithms are inherently parallel
Techniques have recently been developed for the extraction of
rules from trained neural networks
9. 9
A Multi-Layer Feed-Forward Neural Network
Output layer
Input layer
Hidden layer
Output vector
Input vector: X
wij
ij
k
i
i
k
j
k
j x
y
y
w
w )
ˆ
( )
(
)
(
)
1
(
10. 10
How A Multi-Layer Neural Network Works
The inputs to the network correspond to the attributes measured
for each training tuple
Inputs are fed simultaneously into the units making up the input
layer
They are then weighted and fed simultaneously to a hidden layer
The number of hidden layers is arbitrary, although usually only one
The weighted outputs of the last hidden layer are input to units
making up the output layer, which emits the network's prediction
The network is feed-forward: None of the weights cycles back to
an input unit or to an output unit of a previous layer
From a statistical point of view, networks perform nonlinear
regression: Given enough hidden units and enough training
samples, they can closely approximate any function
11. 11
Defining a Network Topology
Decide the network topology: Specify # of units in the
input layer, # of hidden layers (if > 1), # of units in each
hidden layer, and # of units in the output layer
Normalize the input values for each attribute measured in
the training tuples to [0.0—1.0]
One input unit per domain value, each initialized to 0
Output, if for classification and more than two classes,
one output unit per class is used
Once a network has been trained and its accuracy is
unacceptable, repeat the training process with a different
network topology or a different set of initial weights
12. 12
Backpropagation
Iteratively process a set of training tuples & compare the network's
prediction with the actual known target value
For each training tuple, the weights are modified to minimize the
mean squared error between the network's prediction and the actual
target value
Modifications are made in the “backwards” direction: from the output
layer, through each hidden layer down to the first hidden layer, hence
“backpropagation”
Steps
Initialize weights to small random numbers, associated with biases
Propagate the inputs forward (by applying activation function)
Backpropagate the error (by updating weights and biases)
Terminating condition (when error is very small, etc.)
13. 13
Neuron: A Hidden/Output Layer Unit
An n-dimensional input vector x is mapped into variable y by means of the
scalar product and a nonlinear function mapping
The inputs to unit are outputs from the previous layer. They are multiplied by
their corresponding weights to form a weighted sum, which is added to the
bias associated with unit. Then a nonlinear activation function is applied to it.
mk
f
weighted
sum
Input
vector x
output y
Activation
function
weight
vector w
w0
w1
wn
x0
x1
xn
)
sign(
y
Example
For
n
0
i
k
i
i x
w m
bias
14. 14
Efficiency and Interpretability
Efficiency of backpropagation: Each epoch (one iteration through the
training set) takes O(|D| * w), with |D| tuples and w weights, but # of
epochs can be exponential to n, the number of inputs, in worst case
For easier comprehension: Rule extraction by network pruning
Simplify the network structure by removing weighted links that
have the least effect on the trained network
Then perform link, unit, or activation value clustering
The set of input and activation values are studied to derive rules
describing the relationship between the input and hidden unit
layers
Sensitivity analysis: assess the impact that a given input variable
has on a network output. The knowledge gained from this analysis
can be represented in rules
15. 15
Chapter 9. Classification: Advanced Methods
Bayesian Belief Networks
Classification by Backpropagation
Support Vector Machines
Classification by Using Frequent Patterns
Lazy Learners (or Learning from Your Neighbors)
Other Classification Methods
Additional Topics Regarding Classification
Summary
16. 16
Classification: A Mathematical Mapping
Classification: predicts categorical class labels
E.g., Personal homepage classification
xi = (x1, x2, x3, …), yi = +1 or –1
x1 : # of word “homepage”
x2 : # of word “welcome”
Mathematically, x X = n, y Y = {+1, –1},
We want to derive a function f: X Y
Linear Classification
Binary Classification problem
Data above the red line belongs to class ‘x’
Data below red line belongs to class ‘o’
Examples: SVM, Perceptron, Probabilistic Classifiers
x
x
x
x
x
x
x
x
x
x oo
o
o
o
o
o
o
o o
o
o
o
17. 17
Discriminative Classifiers
Advantages
Prediction accuracy is generally high
As compared to Bayesian methods – in general
Robust, works when training examples contain errors
Fast evaluation of the learned target function
Bayesian networks are normally slow
Criticism
Long training time
Difficult to understand the learned function (weights)
Bayesian networks can be used easily for pattern
discovery
Not easy to incorporate domain knowledge
Easy in the form of priors on the data or distributions
18. 18
SVM—Support Vector Machines
A relatively new classification method for both linear and
nonlinear data
It uses a nonlinear mapping to transform the original
training data into a higher dimension
With the new dimension, it searches for the linear optimal
separating hyperplane (i.e., “decision boundary”)
With an appropriate nonlinear mapping to a sufficiently
high dimension, data from two classes can always be
separated by a hyperplane
SVM finds this hyperplane using support vectors
(“essential” training tuples) and margins (defined by the
support vectors)
19. 19
SVM—History and Applications
Vapnik and colleagues (1992)—groundwork from Vapnik
& Chervonenkis’ statistical learning theory in 1960s
Features: training can be slow but accuracy is high owing
to their ability to model complex nonlinear decision
boundaries (margin maximization)
Used for: classification and numeric prediction
Applications:
handwritten digit recognition, object recognition,
speaker identification, benchmarking time-series
prediction tests
21. September 24, 2023 Data Mining: Concepts and Techniques 21
SVM—Margins and Support Vectors
22. 22
SVM—When Data Is Linearly Separable
m
Let data D be (X1, y1), …, (X|D|, y|D|), where Xi is the set of training tuples
associated with the class labels yi
There are infinite lines (hyperplanes) separating the two classes but we want to
find the best one (the one that minimizes classification error on unseen data)
SVM searches for the hyperplane with the largest margin, i.e., maximum
marginal hyperplane (MMH)
23. 23
SVM—Linearly Separable
A separating hyperplane can be written as
W ● X + b = 0
where W={w1, w2, …, wn} is a weight vector and b a scalar (bias)
For 2-D it can be written as
w0 + w1 x1 + w2 x2 = 0
The hyperplane defining the sides of the margin:
H1: w0 + w1 x1 + w2 x2 ≥ 1 for yi = +1, and
H2: w0 + w1 x1 + w2 x2 ≤ – 1 for yi = –1
Any training tuples that fall on hyperplanes H1 or H2 (i.e., the
sides defining the margin) are support vectors
This becomes a constrained (convex) quadratic optimization
problem: Quadratic objective function and linear constraints
Quadratic Programming (QP) Lagrangian multipliers
24. 24
Why Is SVM Effective on High Dimensional Data?
The complexity of trained classifier is characterized by the # of
support vectors rather than the dimensionality of the data
The support vectors are the essential or critical training examples —
they lie closest to the decision boundary (MMH)
If all other training examples are removed and the training is
repeated, the same separating hyperplane would be found
The number of support vectors found can be used to compute an
(upper) bound on the expected error rate of the SVM classifier, which
is independent of the data dimensionality
Thus, an SVM with a small number of support vectors can have good
generalization, even when the dimensionality of the data is high
25. 25
SVM—Linearly Inseparable
Transform the original input data into a higher dimensional
space
Search for a linear separating hyperplane in the new space
A1
A2
26. 26
SVM: Different Kernel functions
Instead of computing the dot product on the transformed
data, it is math. equivalent to applying a kernel function
K(Xi, Xj) to the original data, i.e., K(Xi, Xj) = Φ(Xi) Φ(Xj)
Typical Kernel Functions
SVM can also be used for classifying multiple (> 2) classes
and for regression analysis (with additional parameters)
27. 27
Scaling SVM by Hierarchical Micro-Clustering
SVM is not scalable to the number of data objects in terms of training
time and memory usage
H. Yu, J. Yang, and J. Han, “Classifying Large Data Sets Using SVM
with Hierarchical Clusters”, KDD'03)
CB-SVM (Clustering-Based SVM)
Given limited amount of system resources (e.g., memory),
maximize the SVM performance in terms of accuracy and the
training speed
Use micro-clustering to effectively reduce the number of points to
be considered
At deriving support vectors, de-cluster micro-clusters near
“candidate vector” to ensure high classification accuracy
28. 28
CF-Tree: Hierarchical Micro-cluster
Read the data set once, construct a statistical summary of the data
(i.e., hierarchical clusters) given a limited amount of memory
Micro-clustering: Hierarchical indexing structure
provide finer samples closer to the boundary and coarser
samples farther from the boundary
29. 29
Selective Declustering: Ensure High Accuracy
CF tree is a suitable base structure for selective declustering
De-cluster only the cluster Ei such that
Di – Ri < Ds, where Di is the distance from the boundary to the
center point of Ei and Ri is the radius of Ei
Decluster only the cluster whose subclusters have possibilities to be
the support cluster of the boundary
“Support cluster”: The cluster whose centroid is a support vector
30. 30
CB-SVM Algorithm: Outline
Construct two CF-trees from positive and negative data
sets independently
Need one scan of the data set
Train an SVM from the centroids of the root entries
De-cluster the entries near the boundary into the next
level
The children entries de-clustered from the parent
entries are accumulated into the training set with the
non-declustered parent entries
Train an SVM again from the centroids of the entries in
the training set
Repeat until nothing is accumulated
31. 31
Accuracy and Scalability on Synthetic Dataset
Experiments on large synthetic data sets shows better
accuracy than random sampling approaches and far more
scalable than the original SVM algorithm
32. 32
SVM vs. Neural Network
SVM
Deterministic algorithm
Nice generalization
properties
Hard to learn – learned
in batch mode using
quadratic programming
techniques
Using kernels can learn
very complex functions
Neural Network
Nondeterministic
algorithm
Generalizes well but
doesn’t have strong
mathematical foundation
Can easily be learned in
incremental fashion
To learn complex
functions—use multilayer
perceptron (nontrivial)
33. 33
SVM Related Links
SVM Website: http://www.kernel-machines.org/
Representative implementations
LIBSVM: an efficient implementation of SVM, multi-
class classifications, nu-SVM, one-class SVM, including
also various interfaces with java, python, etc.
SVM-light: simpler but performance is not better than
LIBSVM, support only binary classification and only in C
SVM-torch: another recent implementation also
written in C
34. 34
Chapter 9. Classification: Advanced Methods
Bayesian Belief Networks
Classification by Backpropagation
Support Vector Machines
Classification by Using Frequent Patterns
Lazy Learners (or Learning from Your Neighbors)
Other Classification Methods
Additional Topics Regarding Classification
Summary
35. 35
Associative Classification
Associative classification: Major steps
Mine data to find strong associations between frequent patterns
(conjunctions of attribute-value pairs) and class labels
Association rules are generated in the form of
P1 ^ p2 … ^ pl “Aclass = C” (conf, sup)
Organize the rules to form a rule-based classifier
Why effective?
It explores highly confident associations among multiple attributes
and may overcome some constraints introduced by decision-tree
induction, which considers only one attribute at a time
Associative classification has been found to be often more accurate
than some traditional classification methods, such as C4.5
36. 36
Typical Associative Classification Methods
CBA (Classification Based on Associations: Liu, Hsu & Ma, KDD’98)
Mine possible association rules in the form of
Cond-set (a set of attribute-value pairs) class label
Build classifier: Organize rules according to decreasing precedence
based on confidence and then support
CMAR (Classification based on Multiple Association Rules: Li, Han, Pei,
ICDM’01)
Classification: Statistical analysis on multiple rules
CPAR (Classification based on Predictive Association Rules: Yin & Han, SDM’03)
Generation of predictive rules (FOIL-like analysis) but allow covered
rules to retain with reduced weight
Prediction using best k rules
High efficiency, accuracy similar to CMAR
37. 37
Frequent Pattern-Based Classification
H. Cheng, X. Yan, J. Han, and C.-W. Hsu, “Discriminative
Frequent Pattern Analysis for Effective Classification”,
ICDE'07
Accuracy issue
Increase the discriminative power
Increase the expressive power of the feature space
Scalability issue
It is computationally infeasible to generate all feature
combinations and filter them with an information gain
threshold
Efficient method (DDPMine: FPtree pruning): H. Cheng,
X. Yan, J. Han, and P. S. Yu, "Direct Discriminative
Pattern Mining for Effective Classification", ICDE'08
38. 38
Frequent Pattern vs. Single Feature
(a) Austral (c) Sonar
(b) Cleve
Fig. 1. Information Gain vs. Pattern Length
The discriminative power of some frequent patterns is
higher than that of single features.
39. 39
Empirical Results
0 100 200 300 400 500 600 700
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
InfoGain
IG_UpperBnd
Support
Information
Gain
(a) Austral (c) Sonar
(b) Breast
Fig. 2. Information Gain vs. Pattern Frequency
40. 40
Feature Selection
Given a set of frequent patterns, both non-discriminative
and redundant patterns exist, which can cause overfitting
We want to single out the discriminative patterns and
remove redundant ones
The notion of Maximal Marginal Relevance (MMR) is
borrowed
A document has high marginal relevance if it is both
relevant to the query and contains minimal marginal
similarity to previously selected documents
43. 43
DDPMine: Branch-and-Bound Search
Association between information
gain and frequency
a
b
a: constant, a parent
node
b: variable, a descendent
)
sup(
)
sup( parent
child
)
sup(
)
sup( a
b
45. 45
Chapter 9. Classification: Advanced Methods
Bayesian Belief Networks
Classification by Backpropagation
Support Vector Machines
Classification by Using Frequent Patterns
Lazy Learners (or Learning from Your Neighbors)
Other Classification Methods
Additional Topics Regarding Classification
Summary
46. 46
Lazy vs. Eager Learning
Lazy vs. eager learning
Lazy learning (e.g., instance-based learning): Simply
stores training data (or only minor processing) and
waits until it is given a test tuple
Eager learning (the above discussed methods): Given
a set of training tuples, constructs a classification model
before receiving new (e.g., test) data to classify
Lazy: less time in training but more time in predicting
Accuracy
Lazy method effectively uses a richer hypothesis space
since it uses many local linear functions to form an
implicit global approximation to the target function
Eager: must commit to a single hypothesis that covers
the entire instance space
47. 47
Lazy Learner: Instance-Based Methods
Instance-based learning:
Store training examples and delay the processing
(“lazy evaluation”) until a new instance must be
classified
Typical approaches
k-nearest neighbor approach
Instances represented as points in a Euclidean
space.
Locally weighted regression
Constructs local approximation
Case-based reasoning
Uses symbolic representations and knowledge-
based inference
48. 48
The k-Nearest Neighbor Algorithm
All instances correspond to points in the n-D space
The nearest neighbor are defined in terms of
Euclidean distance, dist(X1, X2)
Target function could be discrete- or real- valued
For discrete-valued, k-NN returns the most common
value among the k training examples nearest to xq
Vonoroi diagram: the decision surface induced by 1-
NN for a typical set of training examples
.
_
+
_ xq
+
_ _
+
_
_
+
.
.
.
. .
49. 49
Discussion on the k-NN Algorithm
k-NN for real-valued prediction for a given unknown tuple
Returns the mean values of the k nearest neighbors
Distance-weighted nearest neighbor algorithm
Weight the contribution of each of the k neighbors
according to their distance to the query xq
Give greater weight to closer neighbors
Robust to noisy data by averaging k-nearest neighbors
Curse of dimensionality: distance between neighbors could
be dominated by irrelevant attributes
To overcome it, axes stretch or elimination of the least
relevant attributes
2
)
,
(
1
i
x
q
x
d
w
50. 50
Case-Based Reasoning (CBR)
CBR: Uses a database of problem solutions to solve new problems
Store symbolic description (tuples or cases)—not points in a Euclidean
space
Applications: Customer-service (product-related diagnosis), legal ruling
Methodology
Instances represented by rich symbolic descriptions (e.g., function
graphs)
Search for similar cases, multiple retrieved cases may be combined
Tight coupling between case retrieval, knowledge-based reasoning,
and problem solving
Challenges
Find a good similarity metric
Indexing based on syntactic similarity measure, and when failure,
backtracking, and adapting to additional cases
51. 51
Chapter 9. Classification: Advanced Methods
Bayesian Belief Networks
Classification by Backpropagation
Support Vector Machines
Classification by Using Frequent Patterns
Lazy Learners (or Learning from Your Neighbors)
Other Classification Methods
Additional Topics Regarding Classification
Summary
52. 52
Genetic Algorithms (GA)
Genetic Algorithm: based on an analogy to biological evolution
An initial population is created consisting of randomly generated rules
Each rule is represented by a string of bits
E.g., if A1 and ¬A2 then C2 can be encoded as 100
If an attribute has k > 2 values, k bits can be used
Based on the notion of survival of the fittest, a new population is
formed to consist of the fittest rules and their offspring
The fitness of a rule is represented by its classification accuracy on a
set of training examples
Offspring are generated by crossover and mutation
The process continues until a population P evolves when each rule in P
satisfies a prespecified threshold
Slow but easily parallelizable
53. 53
Rough Set Approach
Rough sets are used to approximately or “roughly” define
equivalent classes
A rough set for a given class C is approximated by two sets: a lower
approximation (certain to be in C) and an upper approximation
(cannot be described as not belonging to C)
Finding the minimal subsets (reducts) of attributes for feature
reduction is NP-hard but a discernibility matrix (which stores the
differences between attribute values for each pair of data tuples) is
used to reduce the computation intensity
54. 54
Fuzzy Set
Approaches
Fuzzy logic uses truth values between 0.0 and 1.0 to represent the
degree of membership (such as in a fuzzy membership graph)
Attribute values are converted to fuzzy values. Ex.:
Income, x, is assigned a fuzzy membership value to each of the
discrete categories {low, medium, high}, e.g. $49K belongs to
“medium income” with fuzzy value 0.15 but belongs to “high
income” with fuzzy value 0.96
Fuzzy membership values do not have to sum to 1.
Each applicable rule contributes a vote for membership in the
categories
Typically, the truth values for each predicted category are summed,
and these sums are combined
55. 55
Chapter 9. Classification: Advanced Methods
Bayesian Belief Networks
Classification by Backpropagation
Support Vector Machines
Classification by Using Frequent Patterns
Lazy Learners (or Learning from Your Neighbors)
Other Classification Methods
Additional Topics Regarding Classification
Summary
56. Multiclass Classification
Classification involving more than two classes (i.e., > 2 Classes)
Method 1. One-vs.-all (OVA): Learn a classifier one at a time
Given m classes, train m classifiers: one for each class
Classifier j: treat tuples in class j as positive & all others as negative
To classify a tuple X, the set of classifiers vote as an ensemble
Method 2. All-vs.-all (AVA): Learn a classifier for each pair of classes
Given m classes, construct m(m-1)/2 binary classifiers
A classifier is trained using tuples of the two classes
To classify a tuple X, each classifier votes. X is assigned to the
class with maximal vote
Comparison
All-vs.-all tends to be superior to one-vs.-all
Problem: Binary classifier is sensitive to errors, and errors affect
vote count
56
57. Error-Correcting Codes for Multiclass Classification
Originally designed to correct errors during data
transmission for communication tasks by exploring
data redundancy
Example
A 7-bit codeword associated with classes 1-4
57
Class Error-Corr. Codeword
C1 1 1 1 1 1 1 1
C2 0 0 0 0 1 1 1
C3 0 0 1 1 0 0 1
C4 0 1 0 1 0 1 0
Given a unknown tuple X, the 7-trained classifiers output: 0001010
Hamming distance: # of different bits between two codewords
H(X, C1) = 5, by checking # of bits between [1111111] & [0001010]
H(X, C2) = 3, H(X, C3) = 3, H(X, C4) = 1, thus C4 as the label for X
Error-correcting codes can correct up to (h-1)/h 1-bit error, where h is
the minimum Hamming distance between any two codewords
If we use 1-bit per class, it is equiv. to one-vs.-all approach, the code
are insufficient to self-correct
When selecting error-correcting codes, there should be good row-wise
and col.-wise separation between the codewords
58. Semi-Supervised Classification
Semi-supervised: Uses labeled and unlabeled data to build a classifier
Self-training:
Build a classifier using the labeled data
Use it to label the unlabeled data, and those with the most confident
label prediction are added to the set of labeled data
Repeat the above process
Adv: easy to understand; disadv: may reinforce errors
Co-training: Use two or more classifiers to teach each other
Each learner uses a mutually independent set of features of each
tuple to train a good classifier, say f1
Then f1 and f2 are used to predict the class label for unlabeled data
X
Teach each other: The tuple having the most confident prediction
from f1 is added to the set of labeled data for f2, & vice versa
Other methods, e.g., joint probability distribution of features and labels
58
59. Active Learning
Class labels are expensive to obtain
Active learner: query human (oracle) for labels
Pool-based approach: Uses a pool of unlabeled data
L: a small subset of D is labeled, U: a pool of unlabeled data in D
Use a query function to carefully select one or more tuples from U
and request labels from an oracle (a human annotator)
The newly labeled samples are added to L, and learn a model
Goal: Achieve high accuracy using as few labeled data as possible
Evaluated using learning curves: Accuracy as a function of the number
of instances queried (# of tuples to be queried should be small)
Research issue: How to choose the data tuples to be queried?
Uncertainty sampling: choose the least certain ones
Reduce version space, the subset of hypotheses consistent w. the
training data
Reduce expected entropy over U: Find the greatest reduction in
the total number of incorrect predictions
59
60. Transfer Learning: Conceptual Framework
Transfer learning: Extract knowledge from one or more source tasks
and apply the knowledge to a target task
Traditional learning: Build a new classifier for each new task
Transfer learning: Build new classifier by applying existing knowledge
learned from source tasks
60
Traditional Learning Framework Transfer Learning Framework
61. Transfer Learning: Methods and Applications
Applications: Especially useful when data is outdated or distribution
changes, e.g., Web document classification, e-mail spam filtering
Instance-based transfer learning: Reweight some of the data from
source tasks and use it to learn the target task
TrAdaBoost (Transfer AdaBoost)
Assume source and target data each described by the same set of
attributes (features) & class labels, but rather diff. distributions
Require only labeling a small amount of target data
Use source data in training: When a source tuple is misclassified,
reduce the weight of such tupels so that they will have less effect on
the subsequent classifier
Research issues
Negative transfer: When it performs worse than no transfer at all
Heterogeneous transfer learning: Transfer knowledge from different
feature space or multiple source domains
Large-scale transfer learning
61
62. 62
Chapter 9. Classification: Advanced Methods
Bayesian Belief Networks
Classification by Backpropagation
Support Vector Machines
Classification by Using Frequent Patterns
Lazy Learners (or Learning from Your Neighbors)
Other Classification Methods
Additional Topics Regarding Classification
Summary
63. 63
Summary
Effective and advanced classification methods
Bayesian belief network (probabilistic networks)
Backpropagation (Neural networks)
Support Vector Machine (SVM)
Pattern-based classification
Other classification methods: lazy learners (KNN, case-based
reasoning), genetic algorithms, rough set and fuzzy set approaches
Additional Topics on Classification
Multiclass classification
Semi-supervised classification
Active learning
Transfer learning
66. 66
What Is Prediction?
(Numerical) prediction is similar to classification
construct a model
use model to predict continuous or ordered value for a given input
Prediction is different from classification
Classification refers to predict categorical class label
Prediction models continuous-valued functions
Major method for prediction: regression
model the relationship between one or more independent or
predictor variables and a dependent or response variable
Regression analysis
Linear and multiple regression
Non-linear regression
Other regression methods: generalized linear model, Poisson
regression, log-linear models, regression trees
67. 67
Linear Regression
Linear regression: involves a response variable y and a single
predictor variable x
y = w0 + w1 x
where w0 (y-intercept) and w1 (slope) are regression coefficients
Method of least squares: estimates the best-fitting straight line
Multiple linear regression: involves more than one predictor variable
Training data is of the form (X1, y1), (X2, y2),…, (X|D|, y|D|)
Ex. For 2-D data, we may have: y = w0 + w1 x1+ w2 x2
Solvable by extension of least square method or using SAS, S-Plus
Many nonlinear functions can be transformed into the above
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68. 68
Some nonlinear models can be modeled by a polynomial
function
A polynomial regression model can be transformed into
linear regression model. For example,
y = w0 + w1 x + w2 x2 + w3 x3
convertible to linear with new variables: x2 = x2, x3= x3
y = w0 + w1 x + w2 x2 + w3 x3
Other functions, such as power function, can also be
transformed to linear model
Some models are intractable nonlinear (e.g., sum of
exponential terms)
possible to obtain least square estimates through
extensive calculation on more complex formulae
Nonlinear Regression
69. 69
Generalized linear model:
Foundation on which linear regression can be applied to modeling
categorical response variables
Variance of y is a function of the mean value of y, not a constant
Logistic regression: models the prob. of some event occurring as a
linear function of a set of predictor variables
Poisson regression: models the data that exhibit a Poisson
distribution
Log-linear models: (for categorical data)
Approximate discrete multidimensional prob. distributions
Also useful for data compression and smoothing
Regression trees and model trees
Trees to predict continuous values rather than class labels
Other Regression-Based Models
70. 70
Regression Trees and Model Trees
Regression tree: proposed in CART system (Breiman et al. 1984)
CART: Classification And Regression Trees
Each leaf stores a continuous-valued prediction
It is the average value of the predicted attribute for the training
tuples that reach the leaf
Model tree: proposed by Quinlan (1992)
Each leaf holds a regression model—a multivariate linear equation
for the predicted attribute
A more general case than regression tree
Regression and model trees tend to be more accurate than linear
regression when the data are not represented well by a simple linear
model
71. 71
Predictive modeling: Predict data values or construct
generalized linear models based on the database data
One can only predict value ranges or category distributions
Method outline:
Minimal generalization
Attribute relevance analysis
Generalized linear model construction
Prediction
Determine the major factors which influence the prediction
Data relevance analysis: uncertainty measurement,
entropy analysis, expert judgement, etc.
Multi-level prediction: drill-down and roll-up analysis
Predictive Modeling in Multidimensional Databases
74. 74
SVM—Introductory Literature
“Statistical Learning Theory” by Vapnik: extremely hard to
understand, containing many errors too.
C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern
Recognition. Knowledge Discovery and Data Mining, 2(2), 1998.
Better than the Vapnik’s book, but still written too hard for
introduction, and the examples are so not-intuitive
The book “An Introduction to Support Vector Machines” by N.
Cristianini and J. Shawe-Taylor
Also written hard for introduction, but the explanation about the
mercer’s theorem is better than above literatures
The neural network book by Haykins
Contains one nice chapter of SVM introduction
75. 75
Notes about SVM—
Introductory Literature
“Statistical Learning Theory” by Vapnik: difficult to understand,
containing many errors.
C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern
Recognition. Knowledge Discovery and Data Mining, 2(2), 1998.
Easier than Vapnik’s book, but still not introductory level; the
examples are not so intuitive
The book An Introduction to Support Vector Machines by Cristianini
and Shawe-Taylor
Not introductory level, but the explanation about Mercer’s
Theorem is better than above literatures
Neural Networks and Learning Machines by Haykin
Contains a nice chapter on SVM introduction
77. 77
A Closer Look at CMAR
CMAR (Classification based on Multiple Association Rules: Li, Han, Pei, ICDM’01)
Efficiency: Uses an enhanced FP-tree that maintains the distribution of
class labels among tuples satisfying each frequent itemset
Rule pruning whenever a rule is inserted into the tree
Given two rules, R1 and R2, if the antecedent of R1 is more general
than that of R2 and conf(R1) ≥ conf(R2), then prune R2
Prunes rules for which the rule antecedent and class are not
positively correlated, based on a χ2 test of statistical significance
Classification based on generated/pruned rules
If only one rule satisfies tuple X, assign the class label of the rule
If a rule set S satisfies X, CMAR
divides S into groups according to class labels
uses a weighted χ2 measure to find the strongest group of rules,
based on the statistical correlation of rules within a group
assigns X the class label of the strongest group
78. 78
Perceptron & Winnow
• Vector: x, w
• Scalar: x, y, w
Input: {(x1, y1), …}
Output: classification function f(x)
f(xi) > 0 for yi = +1
f(xi) < 0 for yi = -1
f(x) => wx + b = 0
or w1x1+w2x2+b = 0
x1
x2
• Perceptron: update W
additively
• Winnow: update W
multiplicatively