Scalable Pattern Recognition Algorithms: Applications in Computational Biology and Bioinformatics
mar 2014 · Springer Science & Business Media
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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.
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 t...
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O autorze
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.
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.
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