Introduction to Statistical Pattern Recognition, Second Edition (Computer Science and Scientific Computing Series) (Hardcover)

by Keinosuke Fukunaga (Author) "This book presents and discusses the fundamental mathematical tools for statistical decision-making processes in pattern recognition..." (more)
Key Phrases: invariant under any orthonormal transformation, reclassification algorithm, optimum linear classifier, New York, Repeat Experiment, Pattern Anal (more...)

Editorial Reviews

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

Product Details

• Hardcover: 592 pages
• Publisher: Academic Press; 2 edition (October 12, 1990)
• Language: English
• ISBN-10: 0122698517
• ISBN-13: 978-0122698514
• Product Dimensions: 9.2 x 6.1 x 1.3 inches
• Shipping Weight: 1 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars See all reviews (5 customer reviews)
• Amazon.com Sales Rank: #339,336 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

30 of 31 people found the following review helpful:

4.0 out of 5 stars pattern recognition in engineering, February 8, 2008

By	Michael R. Chernick "statman31147" (Holland PA) - See all my reviews (REAL NAME)

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.

16 of 17 people found the following review helpful:

3.0 out of 5 stars A best book on Statistical Pattern Recognition, September 12, 2005

By	supercutepig (USA) - See all my reviews

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.

16 of 18 people found the following review helpful:

4.0 out of 5 stars good coverage for engineers, August 3, 2000

By	Michael R. Chernick "statman13" (Malvern, PA) - See all my reviews (REAL NAME)

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 (1972) for good explanations of the fundamental concepts. For statisticians McLachlan's book is now far and away the best source.