Introduction to Statistical Pattern Recognition book cover

Introduction to Statistical Pattern Recognition

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.

Hardbound, 592 Pages

Published: September 1990

Imprint: Academic Press

ISBN: 978-0-12-269851-4

Contents


  • Preface

    Acknowledgments

    Chapter 1 Introduction

    1.1 Formulation of Pattern Recognition Problems

    1.2 Process of Classifier Design

    Notation

    References

    Chapter 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

    References

    Chapter 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

    References

    Chapter 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

    References

    Chapter5 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

    References

    Chapter 6 Nonparametric Density Estimation

    6.1 Parzen Density Estimate

    6.2 kNearest Neighbor Density Estimate

    6.3 Expansion by Basis Functions

    Computer Projects

    Problems

    References

    Chapter 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

    References

    Chapter 8 Successive Parameter Estimation

    8.1 Successive Adjustment of a Linear Classifier

    8.2 Stochastic Approximation

    8.3 Successive Bayes Estimation

    Computer Projects

    Problems

    References

    Chapter 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

    References

    Chapter 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

    References

    Chapter 11 Clustering

    11.1 Parametric Clustering

    11.2 Nonparametric Clustering

    11.3 Selection of Representatives

    Computer Projects

    Problems

    References

    Appendix A Derivatives of Matrices

    Appendix B Mathematical Formulas

    Appendix C Normal Error Table

    Appendix D Gamma Function Table

    Index

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