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Author:   Sergios Theodoridis (Professor of Machine Learning and Signal Processing, National and Kapodistrian University of Athens, Athens, Greece) ,  Aggelos Pikrakis (Lecturer, Department of Informatics, University of Piraeus, Greece) ,  Konstantinos Koutroumbas (Institute for Space Applications & Remote Sensing, National Observatory of Athens, Greece) ,  Dionisis Cavouras
Publisher:   Elsevier Science Publishing Co Inc
Imprint:   Academic Press Inc
Dimensions:   Width: 19.10cm , Height: 1.50cm , Length: 23.50cm
Weight:   0.430kg
ISBN:  

9780123744869

ISBN 10:   0123744865
Pages:   240
Publication Date:   30 April 2010
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Preface Chapter 1. Classifiers Based on Bayes Decision Theory 1.1 Introduction 1.2 Bayes Decision Theory 1.3 The Gaussian Probability Density Function 1.4 Minimum Distance Classifiers 1.4.1 The Euclidean Distance Classifier 1.4.2 The Mahalanobis Distance Classifier 1.4.3 Maximum Likelihood Parameter Estimation of Gaussian pdfs 1.5 Mixture Models 1.6 The Expectation-Maximization Algorithm 1.7 Parzen Windows 1.8 k-Nearest Neighbor Density Estimation 1.9 The Naive Bayes Classifier 1.10 The Nearest Neighbor RuleChapter 2. Classifiers Based on Cost Function Optimization 2.1 Introduction 2.2 The Perceptron Algorithm 2.2.1 The Online Form of the Perceptron Algorithm 2.3 The Sum of Error Squares Classifier 2.3.1 The Multiclass LS Classifier 2.4 Support Vector Machines: The Linear Case 2.4.1 Multiclass Generalizations 2.5 SVM: The Nonlinear Case 2.6 The Kernel Perceptron Algorithm 2.7 The AdaBoost Algorithm 2.8 Multilayer PerceptronsChapter 3. Data Transformation: Feature Generation and Dimensionality Reduction 3.1 Introduction 3.2 Principal Component Analysis 3.3 The Singular Value Decomposition Method 3.4 Fisher's Linear Discriminant Analysis 3.5 The Kernel PCA 3.6 Laplacian EigenmapChapter 4. Feature Selection 4.1 Introduction 4.2 Outlier Removal 4.3 Data Normalization 4.4 Hypothesis Testing: The t-Test 4.5 The Receiver Operating Characteristic Curve 4.6 Fisher's Discriminant Ratio 4.7 Class Separability Measures 4.7.1 Divergence 4.7.2 Bhattacharyya Distance and Chernoff Bound 4.7.3 Measures Based on Scatter Matrices 4.8 Feature Subset Selection 4.8.1 Scalar Feature Selection 4.8.2 Feature Vector SelectionChapter 5. Template Matching 5.1 Introduction 5.2 The Edit Distance 5.3 Matching Sequences of Real Numbers 5.4 Dynamic Time Warping in Speech RecognitionChapter 6. Hidden Markov Models 6.1 Introduction 6.2 Modeling 6.3 Recognition and TrainingChapter 7. Clustering 7.1 Introduction 7.2 Basic Concepts and Definitions 7.3 Clustering Algorithms 7.4 Sequential Algorithms 7.4.1 BSAS Algorithm 7.4.2 Clustering Refinement 7.5 Cost Function Optimization Clustering Algorithms 7.5.1 Hard Clustering Algorithms 7.5.2 Nonhard Clustering Algorithms 7.6 Miscellaneous Clustering Algorithms 7.7 Hierarchical Clustering Algorithms 7.7.1 Generalized Agglomerative Scheme 7.7.2 Specific Agglomerative Clustering Algorithms 7.7.3 Choosing the Best ClusteringAppendixReferencesIndex

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Konstantinos Koutroumbas acquired a degree from the University of Patras, Greece in Computer Engineering and Informatics in 1989, a MSc in Computer Science from the University of London, UK in 1990, and a Ph.D. degree from the University of Athens in 1995. Since 2001 he has been with the Institute for Space Applications and Remote Sensing of the National Observatory of Athens.

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