Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence / Edition 1

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Overview

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks.

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Editorial Reviews

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"This book tries to balance the mixture of theories, algorithms, and applications and is a good reference for people who want to solve a complex optimization problem for their field. ... Overall, this book is well organized and well written. There is no doubt that this is another good pattern recognition reference to have on one's bookshelf." (Zheng Liu, IAPR Newsletter 30(4), October 2008)
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Product Details

  • ISBN-13: 9783540496069
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 4/28/2007
  • Series: Natural Computing Series
  • Edition description: 2007
  • Edition number: 1
  • Pages: 311
  • Product dimensions: 6.30 (w) x 9.50 (h) x 1.00 (d)

Table of Contents

Genetic Algorithms.- Supervised Classification Using Genetic Algorithms.- Theoretical Analysis of the GA-classifier.- Variable String Lengths in GA-classifier.- Chromosome Differentiation in VGA-classifier.- Multiobjective VGA-classifier and Quantitative Indices.- Genetic Algorithms in Clustering.- Genetic Learning in Bioinformatics.- Genetic Algorithms and Web Intelligence.

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