Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence
This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries, and it demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains.

This book will be useful to graduate students and researchers in computer science, electrical engineering, systems science, and information technology, both as a text and reference book. Researchers and practitioners in industry working in system design, control, pattern recognition, data mining, soft computing, bioinformatics and Web intelligence will also benefit.

1117395849
Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence
This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries, and it demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains.

This book will be useful to graduate students and researchers in computer science, electrical engineering, systems science, and information technology, both as a text and reference book. Researchers and practitioners in industry working in system design, control, pattern recognition, data mining, soft computing, bioinformatics and Web intelligence will also benefit.

109.99 In Stock
Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence

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

by Sanghamitra Bandyopadhyay, Sankar Kumar Pal
Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence

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

by Sanghamitra Bandyopadhyay, Sankar Kumar Pal

Paperback(Softcover reprint of hardcover 1st ed. 2007)

$109.99 
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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. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries, and it demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains.

This book will be useful to graduate students and researchers in computer science, electrical engineering, systems science, and information technology, both as a text and reference book. Researchers and practitioners in industry working in system design, control, pattern recognition, data mining, soft computing, bioinformatics and Web intelligence will also benefit.


Product Details

ISBN-13: 9783642080548
Publisher: Springer Berlin Heidelberg
Publication date: 11/23/2010
Series: Natural Computing Series
Edition description: Softcover reprint of hardcover 1st ed. 2007
Pages: 311
Product dimensions: 6.10(w) x 9.25(h) x 0.36(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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