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Genetic Algorithms for Pattern Recognition

Genetic Algorithms for Pattern Recognition

Genetic Algorithms for Pattern Recognition
Stock photo: cover may vary

Genetic Algorithms for Pattern Recognition Hardback - 1996 - 1st Edition

by Pal, S. K. and Wang, P. P. (eds.)

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  • Hardback

Description

CRC Press, 1996. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:9780849394676
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Details

  • Title Genetic Algorithms for Pattern Recognition
  • Author Pal, S. K. and Wang, P. P. (eds.)
  • Binding Hardback
  • Edition number 1st
  • Edition 1
  • Pages 336
  • Volumes 1
  • Language ENG
  • Publisher CRC Press, Boca Raton
  • Publication date 1996
  • Bookseller's Inventory # 9556570
  • ISBN 9780849394676 / 0849394678
  • Weight 1.42 lbs (0.64 kg)
  • Dimensions 9.56 x 6.42 x 0.95 in (24.28 x 16.31 x 2.41 cm)
  • Category Computers - Other Applications
  • Library of Congress subjects Pattern perception, Machine learning
  • Library of Congress Catalogue Number 95046195
  • Dewey Decimal Code 006.4

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Reader reviews for Genetic Algorithms for Pattern Recognition

From the publisher

Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems.
The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers.
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