Scalable Pattern Recognition Algorithms
Applications in Computational Biology and Bioinformatics
Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Computer Science, Science & Nature, Science
| 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.
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.
