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Introduction to Statistical Pattern Recognition Fukunaga,Keinosu....pdf

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    Cover
    Frontmatter
    Half Title Page
    Title Page
    Copyright
    Dedication
    Table of Contents
    Preface
    Acknowledgments
    Chapter 1: Introduction
    1.1 Formulation of Pattern Recognition Problems
    1.2 Process of Classifier Design
    Notation 1
    References 1
    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 2
    Problems 2
    References 2
    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 3
    Problems 3
    References 3
    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 4
    Problems 4
    References 4
    Chapter 5: 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 5
    Problems 5
    References 5
    Chapter 6: Nonparametric Density Estimation
    6.1 Parzen Density Estimate
    6.2 k Nearest Neighbor Density Estimate
    6.3 Expansion by Basis Functions
    Computer Projects 6
    Problems 6
    References 6
    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 7
    Problems 7
    References 7
    Chapter 8: Successive Parameter Estimation
    8.1 Successive Adjustment of a Linear Classifier
    8.2 Stochastic Approximation
    8.3 Successive Bayes Estimation
    Computer Projects 8
    Problems 8
    References 8
    Chapter 9: Feature Extraction and Linear Mapping for Signal Representation
    9.1 The Discrete Karhunen-Love Expansion
    9.2 The Karhunen-Love Expansion for Random Processes
    9.3 Estimation of Eigenvalues and Eigenvectors
    Computer Projects 9
    Problems 9
    References 9
    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
    10.5 Feature Subset Selection
    Computer Projects 10
    Problems 10
    References 10
    Chapter 11: Clustering
    11.1 Parametric Clustering
    11.2 Nonparametric Clustering
    11.3 Selection of Representatives
    Computer Projects 11
    Problems 11
    References 11
    Backmatter
    Appendix A: Derivatives of Matrices
    Appendix B: Mathematical Formulas
    Appendix C: Normal Error Table
    Appendix D: Gamma Function Table
    Index
    About the Author
    Back Cover
    Introduction to Statistical Pattern Recogni-tion % % Second Edition 0 0 0 0 0 n
    Keinosuke Fukunaga Introduction to Stas-tical Pattern Recognit ion Second Edition This completely revised second edition presents an introduction to statistical pat- tern 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 estima- tion, which are the main subjects of this book, are regarded as fimdamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a ref- erence book for people who work in the field. Each chapter also contains computer projects as well as exercises.
    Introduction to Statistical Pattern Recognition Second Edition
    This is a volume in COMPUTER SCIENCE AND SCIENTIFIC COMPUTING Editor: WERNER RHEINBOLDT
    Introduction to Statistical Pattern Recognition Second Edition Keinosuke F'ukunaga School of Electrical Engineering Purdue University West Lafa yet te, Indiana M K4 Morgan K.ufmu\n is an imprint of Academic Rars A H a m r t SaenccandTechlndogyCompony San Diego San Francisco New York Boston London Sydney Tokyo
    This book is printed on acid-free paper. 8 Copyright 0 1990 by Academic Press All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system. without permission in writing from the publisher. ACADEMIC PRESS A Harcourt Science and Technology Company 525 B Street, Suite 1900, San Diego, CA 92101-4495 USA h ttp://www.academicpress.com Academic Press 24-28 Oval Road, London N W 1 7DX United Kingdom h tt p:/lwww. hbuWap/ Morgan Kaufmann 340 Pine Street, Sixth Floor, San Francisco, CA 94104-3205 http://mkp.com Library of Congress Cataloging-in-Publication Data Fukunaga. Keinosuke. Fukunaga. - 2nd ed. Introduction to statistical pattern recognition I Keinosuke p, cm. Includes bibliographical references. ISBN 0-12-269851-7 1. Pattern perception - Statistical methods. 2. Decision-making - - Mathematical models. 3. Mathematical statistics. I. Title. 0327.F85 1990 006.4 - dc20 89-18195 CIP PRINTF.D M THE UNITED STATES OF AMEIUCA 03 9
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