Introduction to Statistical Pattern Recognition

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Academic Press, Oct 22, 2013 - Computers - 592 pages
3 Reviews
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

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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.

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