Scalable Pattern Recognition Algorithms: Applications in Computational Biology and Bioinformatics - Hardcover

Maji, Pradipta; Paul, Sushmita

Hardcover

ISBN 10: 3319056298 ISBN 13: 9783319056296

Publisher: Springer, 2014

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 this title

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.

From the Back Cover

Recent advances in high-throughput technologies have resulted in a deluge of biological information. Yet the storage, analysis, and interpretation of such multifaceted data require effective and efficient computational tools.

This unique text/reference 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 book reviews both established and cutting-edge research, following a clear structure reflecting the major phases of a pattern recognition system: classification, feature selection, and clustering. The text provides a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics.

Topics and features:

• Reviews the development of scalable pattern recognition algorithms for computational biology and bioinformatics
• 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

This important work will be of great use to graduate students and researchers in the fields of computer science, electrical and biomedical engineering. Researchers and practitioners involved in pattern recognition, machine learning, computational biology and bioinformatics, data mining, and soft computing will also find the book invaluable.

Edition details

• Publisher: Springer
• Publication date: 2014
• Language: English
• ISBN 10: 3319056298
• ISBN 13: 9783319056296
• Binding: Hardcover
• Number of pages: 326