Introduction to Statistical Pattern Recognition

by Keinosuke Fukunaga

Overview

This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.

Product Details

ISBN-13:	9780080478654
Publisher:	Elsevier Science & Technology Books
Publication date:	10/22/2013
Sold by:	Barnes & Noble
Format:	eBook
Pages:	592
File size:	17 MB
Note:	This product may take a few minutes to download.

Table of Contents

PrefaceAcknowledgmentsChapter 1 Introduction 1.1 Formulation of Pattern Recognition Problems 1.2 Process of Classifier Design Notation ReferencesChapter 2 Random Vectors and Their Properties 2.1 Random Vectors and Their Distributions 2.2 Estimation of Parameters 2.3 Linear Transformation 2.4 Various Properties of Eigenvalues and Eigenvectors Computer Projects Problems ReferencesChapter 3 Hypothesis Testing 3.1 Hypothesis Tests for Two Classes 3.2 Other Hypothesis Tests 3.3 Error Probability in Hypothesis Testing 3.4 Upper Bounds on the Bayes Error 3.5 Sequential Hypothesis Testing Computer Projects Problems ReferencesChapter 4 Parametric Classifiers 4.1 The Bayes Linear Classifier 4.2 Linear Classifier Design 4.3 Quadratic Classifier Design 4.4 Other Classifiers Computer Projects Problems ReferencesChapter5 Parameter Estimation 5.1 Effect of Sample Size in Estimation 5.2 Estimation of Classification Errors 5.3 Holdout, Leave-One-Out, and Resubstitution Methods 5.4 Bootstrap Methods Computer Projects Problems ReferencesChapter 6 Nonparametric Density Estimation 6.1 Parzen Density Estimate 6.2 kNearest Neighbor Density Estimate 6.3 Expansion by Basis Functions Computer Projects Problems ReferencesChapter 7 Nonparametric Classification and Error Estimation 7.1 General Discussion 7.2 Voting kNN Procedure — Asymptotic Analysis 7.3 Voting kNN Procedure — Finite Sample Analysis 7.4 Error Estimation 7.5 Miscellaneous Topics in the kNN Approach Computer Projects Problems ReferencesChapter 8 Successive Parameter Estimation 8.1 Successive Adjustment of a Linear Classifier 8.2 Stochastic Approximation 8.3 Successive Bayes Estimation Computer Projects Problems ReferencesChapter 9 Feature Extraction and Linear Mapping for Signal Representation 9.1 The Discrete Karhunen-Loéve Expansion 9.2 The Karhunen-Loéve Expansion for Random Processes 9.3 Estimation of Eigenvalues and Eigenvectors Computer Projects Problems ReferencesChapter 10 Feature Extraction and Linear Mapping for Classification 10.1 General Problem Formulation 10.2 Discriminant Analysis 10.3 Generalized Criteria 10.4 Nonparametric Discriminant Analysis 10.5 Sequential Selection of Quadratic Features 10.6 Feature Subset Selection Computer Projects Problems ReferencesChapter 11 Clustering 11.1 Parametric Clustering 11.2 Nonparametric Clustering 11.3 Selection of Representatives Computer Projects Problems ReferencesAppendix A Derivatives of MatricesAppendix B Mathematical FormulasAppendix C Normal Error TableAppendix D Gamma Function TableIndex