# Introduction to Statistical Pattern Recognition

Academic Press, Oct 22, 2013 - Computers - 592 pages
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.

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No nonsense! Statistical pattern recognition in its purest form. I love this book.

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I think this book is very useful for people with some statistical background knowledge (maybe not too much), and it contains a lot of basic ideas of many engineering problems, though some of them might need an upgrade for nowadays.

### Contents

 Chapter 1 INTRODUCTION 1 Chapter 2 RANDOM VECTORS AND THEIR PROPERTIES 11 Chapter 3 HYPOTHESIS TESTING 51 Chapter 4 PARAMETRIC CLASSIFIERS 124 Chapter 5 PARAMETER ESTIMATION 181 Chapter 6 NONPARAMETRIC DENSITY ESTIMATION 254 Chapter 7 NONPARAMETRIC CLASSIFICATION AND ERROR ESTIMATION 300 Chapter 8 SUCCESSIVE PARAMETER ESTIMATION 367
 Chapter 10 FEATURE EXTRACTION AND LINEAR MAPPING FOR CLASSIFICATION 441 Chapter 11 CLUSTERING 508 DERIVATIVES OF MATRICES 564 MATHEMATICAL FORMULAS 572 NORMAL ERROR TABLE 576 GAMMA FUNCTION TABLE 578 INDEX 579 Copyright

 Chapter 9 FEATURE EXTRACTION AND LINEAR MAPPING FOR SIGNAL REPRESENTATION 399

### Popular passages

Page 15 - R. 0. Duda and PE Hart, Pattern Classification and Scene Analysis, Wiley, 1972.
Page 43 - Since the determinant of the product of matrices is the product of the determinants...
Page 7 - Thus, pattern recognition, or decision-making in a broader sense, may be considered as a problem of estimating density functions in a high-dimensional space and dividing the space into the regions of categories or classes.