Scalable Pattern Recognition Algorithms

Pradipta Maji

ISBN 10: 3319056298 / ISBN 13: 9783319056296

Published by Springer, Palgrave Macmillan Apr 2014, 2014

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 328 pp. Englisch.

Seller Inventory # 9783319056296

Bibliographic Details

• Title: Scalable Pattern Recognition Algorithms
• Publisher: Springer, Palgrave Macmillan Apr 2014
• Publication Date: 2014
• Language: English
• ISBN 10: 3319056298
• ISBN 13: 9783319056296
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 24D millimeters 241 millimeters width by 160 millimeters height by 24 millimeters depth
• Weight: 658 grams

About this title

Synopsis

This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.

About the Author

Dr. Pradipta Maji is an Associate Professor in the Machine Intelligence Unit at the Indian Statistical Institute, Kolkata, India. Dr. Sushmita Paul is a Research Associate at the same institution.