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Introduction to Statistical Pattern Recognition, Second Edition (Computer Science and Scientific Computing Series) Hardcover – October 12, 1990

ISBN-13: 978-0122698514 ISBN-10: 0122698517 Edition: 2nd

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Product Details

  • Series: Computer Science and Scientific Computing Series
  • Hardcover: 592 pages
  • Publisher: Academic Press; 2 edition (October 12, 1990)
  • Language: English
  • ISBN-10: 0122698517
  • ISBN-13: 978-0122698514
  • Product Dimensions: 1.2 x 6.2 x 9.2 inches
  • Shipping Weight: 1 pounds (View shipping rates and policies)
  • Average Customer Review: 3.7 out of 5 stars  See all reviews (6 customer reviews)
  • Amazon Best Sellers Rank: #441,748 in Books (See Top 100 in Books)

Editorial Reviews

About the Author

By Keinosuke Fukunaga

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Customer Reviews

3.7 out of 5 stars
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Most Helpful Customer Reviews

31 of 32 people found the following review helpful By Michael R. Chernick on February 8, 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.
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17 of 20 people found the following review helpful By supercutepig on September 12, 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.
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10 of 11 people found the following review helpful By A Customer on April 5, 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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