Scalable Pattern Recognition Algorithms: Applications in Computational Biology and Bioinformatics

Maji, Pradipta; Paul, Sushmita

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.

Details

Published by: Springer

Publication Date: 2014-04-04

Format: Hardcover

ISBN-13: 9783319056296

DOI: 10.1007/978-3-319-05630-2

Dimensions: 235cm x155cm

Pages: 304