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 constitutes the refereed proceedings of the 5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013, held in Kolkata, India in December 2013. The 101 revised papers presented together with 9 invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on pattern recognition; machine learning; image processing; speech and video processing; medical imaging; document image processing; soft computing; bioinformatics and computational biology; and social media mining.

The two-volume set of LNCS 11941 and 11942 constitutes the refereed proceedings of the 8th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2019, held in Tezpur, India, in December 2019. The 131 revised full papers presented were carefully reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning; Soft and Evolutionary Computing; Image Processing; Medical Image Processing; Bioinformatics and Biomedical Signal Processing; Information Retrieval; Remote Sensing; Signal and Video Processing; and Smart and Intelligent Sensors.

The two-volume set of LNCS 11941 and 11942 constitutes the refereed proceedings of the 8th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2019, held in Tezpur, India, in December 2019. The 131 revised full papers presented were carefully reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning; Soft and Evolutionary Computing; Image Processing; Medical Image Processing; Bioinformatics and Biomedical Signal Processing; Information Retrieval; Remote Sensing; Signal and Video Processing; and Smart and Intelligent Sensors.

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.