Introduction to Pattern Recognition

Sergios Theodoridis is professor emeritus of machine learning and data processing with the National and Kapodistrian University of Athens, Athens, Greece. He has also served as distinguished professor with the Aalborg University Denmark and as professor with the Chinese University of Hong Kong, Shenzhen, China. In 2023, he received an honorary doctorate degree (D.Sc) from the University of Edinburgh, U.K. He has also received a number of prestigious awards, including the 2014 IEEE Signal Processing Magazine Best Paper Award, the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award, the 2017 European Association for Signal Processing (EURASIP) Athanasios Papoulis Award, the 2014 IEEE Signal Processing Society Carl Friedrich Gauss Education Award, and the 2014 EURASIP Meritorious Service Award. He has served as president of EURASIP and vice president for the IEEE Signal Processing Society. He is a Fellow of EURASIP and a Life Fellow of IEEE. He is the coauthor of the book Pattern Recognition, 4th edition, Academic Press, 2009 and of the book Introduction to Pattern Recognition: A MATLAB Approach, Academic Press, 2010.Aggelos Pikrakis is a Lecturer in the Department of Informatics at the University of Piraeus. His research interests stem from the fields of pattern recognition, audio and image processing, and music information retrieval. He is also the co-author of Introduction to Pattern Recognition: A MATLAB Approach (Academic Press, 2010). 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.

• 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