Overview
- First book to provide a unified framework that describes how genetic learning can be used to design pattern recognition systems
- Includes supplementary material: sn.pub/extras
Part of the book series: Natural Computing Series (NCS)
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Table of contents (10 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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-Ã -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.
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Authors and Affiliations
Bibliographic Information
Book Title: Classification and Learning Using Genetic Algorithms
Book Subtitle: Applications in Bioinformatics and Web Intelligence
Authors: Sanghamitra Bandyopadhyay, Sankar K. Pal
Series Title: Natural Computing Series
DOI: https://doi.org/10.1007/3-540-49607-6
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2007
Hardcover ISBN: 978-3-540-49606-9Published: 23 April 2007
Softcover ISBN: 978-3-642-08054-8Published: 23 November 2010
eBook ISBN: 978-3-540-49607-6Published: 17 May 2007
Series ISSN: 1619-7127
Series E-ISSN: 2627-6461
Edition Number: 1
Number of Pages: XVI, 311
Number of Illustrations: 87 b/w illustrations
Topics: Pattern Recognition, Programming Techniques, Communications Engineering, Networks, Artificial Intelligence, Complex Systems, Computational Biology/Bioinformatics