Introduction to Statistical Pattern Recognition
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Book Overview
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... This description may be from another edition of this product.
Format:Hardcover
Language:English
ISBN:0122698517
ISBN13:9780122698514
Release Date:October 1990
Publisher:Academic Press
Length:626 Pages
Weight:2.28 lbs.
Dimensions:1.3" x 6.2" x 9.2"
Customer Reviews
2 ratings
pattern recognition in engineering
Published by Thriftbooks.com User , 16 years ago
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.
Standard Reference in the Field
Published by Thriftbooks.com User , 24 years ago
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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