Introduction to Statistical Pattern Recognition by Keinosuke FukunagaIntroduction to Statistical Pattern Recognition by Keinosuke Fukunaga

Introduction to Statistical Pattern Recognition

byKeinosuke Fukunaga, Keinosuke Fukunaga

Other | October 22, 2013

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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.

Title:Introduction to Statistical Pattern RecognitionFormat:OtherDimensions:592 pages, 1 × 1 × 1 inPublished:October 22, 2013Publisher:Elsevier ScienceLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0080478654

ISBN - 13:9780080478654

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Table of 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