Scalable Pattern Recognition Algorithms

Applications in Computational Biology and Bioinformatics

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Computer Science, Science & Nature, Science
Cover of the book Scalable Pattern Recognition Algorithms by Sushmita Paul, Pradipta Maji, Springer International Publishing
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Author: Sushmita Paul, Pradipta Maji ISBN: 9783319056302
Publisher: Springer International Publishing Publication: March 19, 2014
Imprint: Springer Language: English
Author: Sushmita Paul, Pradipta Maji
ISBN: 9783319056302
Publisher: Springer International Publishing
Publication: March 19, 2014
Imprint: Springer
Language: English

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.

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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.

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