Introduction to Statistical Pattern Recognition (Google eBook)

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Academic Press, Oct 25, 1990 - Computers - 592 pages
2 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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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.

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This book is very theoretical in nature. It's not a good book for beginners unless they have a very strong background in probability, statistics and linear algebra; even then it may not be the best place to start. The Duda books is much better for people new to pattern recognition.

Contents

Introduction
1
Random Vectors and Their Properties
11
Hypothesis Testing
51
Parametric Classifiers
124
Parameter Estimation
181
Nonparametric Density Estimation
254
Nonparametric Classification and Error Estimation
300
Successive Parameter Estimation
367
Feature Extraction and Linear Mapping for Signal Representation
399
Feature Extraction and Linear Mapping for Classification
441
Clustering
508
Backmatter
564
Back Cover
598
Copyright

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Page 11 - R. 0. Duda and PE Hart, Pattern Classification and Scene Analysis, Wiley, 1972.
Page 39 - Since the determinant of the product of matrices is the product of the determinants...
Page 3 - 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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