| Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence 2007 Edition Contributor(s): Bandyopadhyay, Sanghamitra (Author), Pal, Sankar Kumar (Author) |
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ISBN: 3540496068 ISBN-13: 9783540496069 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: April 2007 Annotation: 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-?-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. |
| Additional Information |
| BISAC Categories: - Computers | Computer Vision & Pattern Recognition - Computers | Computer Science - Computers | Databases - Data Mining |
| Dewey: 004 |
| LCCN: 2007922185 |
| Series: Natural Computing |
| Physical Information: 0.94" H x 6.37" W x 9.36" (1.35 lbs) 311 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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- -vis several widely used classifiers, including neural networks. |