- Hardcover: 592 pages
- Publisher: Academic Press Inc; 2nd Revised edition edition (10 Jan 1992)
- Language English
- ISBN-10: 0122698517
- ISBN-13: 978-0122698514
- Product Dimensions: 23.4 x 15.7 x 3.4 cm
- Amazon Bestsellers Rank: 1,631,918 in Books (See Top 100 in Books)
- See Complete Table of Contents
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Product Description
Product Description
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.
About the Author
By Keinosuke Fukunaga
Inside This Book
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This book presents and discusses the fundamental mathematical tools for statistical decision-making processes in pattern recognition. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
This book presents and discusses the fundamental mathematical tools for statistical decision-making processes in pattern recognition. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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4 reviews
29 of 30 people found the following review helpful
pattern recognition in engineering
9 Feb 2008
Format:Hardcover
Fukunaga is a standard source for pattern recognition methods often cited in the engineering literature. Covers parametric (particularly linear and quadratic discriminant algorithms) and nonparametric methods (density estimation). It is designed for and popular with engineers. When I was working at Nichols Research Corporation Fukunaga's papers and this book (earlier edition) were often cited as sources to justify the algorithms we used for discrimination problems. In fact Fukunaga had been a consultant to the company (used primarily by the Boston branch of the company where the KENN algorithms were developed). It is a reputable source. I still like Duda and Hart (1973) for good explanations of the fundamental concepts. The second edition that was recent ly published with Stark as a third author is also highly recommended. For statisticians McLachlan's book is now far and away the best source.
17 of 19 people found the following review helpful
A best book on Statistical Pattern Recognition
12 Sep 2005
Format:Hardcover
Multivariate analysis is borrowed to name a NEW subject, Statistical Pattern Recognition (SPR). Many statisticians think it unfair or a shame. In spite of these, it is a good reference book of SPR. :-)
[1] Many contents of this book can be found in any graduate textbook of Multivariate Analysis, for instance, Fisher's linear disciminant, etc.
[2] The book is badly printed. Why not using LaTeX?
[3] Guassian distribution is assumed here and there.
[4] It may be good as a reference book, but definitely not as a textbook.
[1] Many contents of this book can be found in any graduate textbook of Multivariate Analysis, for instance, Fisher's linear disciminant, etc.
[2] The book is badly printed. Why not using LaTeX?
[3] Guassian distribution is assumed here and there.
[4] It may be good as a reference book, but definitely not as a textbook.
10 of 11 people found the following review helpful
Standard Reference in the Field
6 April 2000
Format:Hardcover
If you are writing a machine learning paper, and need to cite something to support an argument, you can almost always cite Fukunaga. This work is a standard reference in the field. The presentation of most material is very terse, but that is great if you already have a good feel for the material and need to look up some details about some algorithm or technique. There isn't much about neural networks here, but for the rest of the pattern recognition techniques, this is almost always the first place to start. Another strong point for this book is the use of realistic examples, which illustrate many of the statistical techniques.
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