Overview
- Reviews the development of scalable pattern recognition algorithms for computational biology and bioinformatics
- Includes numerous examples and experimental results to support the theoretical concepts described
- Concludes each chapter with directions for future research and a comprehensive bibliography
- Includes supplementary material: sn.pub/extras
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Table of contents (11 chapters)
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Feature Selection
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Clustering
Keywords
About this book
Reviews
From the book reviews:
“This book provides unique insights into how various soft computing and machine learning methods can be formulated and used in building efficient pattern recognition models. … This is a great resource to students and researchers in the fields of computer science, electrical and biomedical engineering. The author has explained the complex ideas through numerous examples which make conceptualization easy. … The well-organized chapters as well as use of different notations and typescripts make it a user-friendly reference.” (Parthiv Amin, Doody’s Book Reviews, August, 2014)
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Scalable Pattern Recognition Algorithms
Book Subtitle: Applications in Computational Biology and Bioinformatics
Authors: Pradipta Maji, Sushmita Paul
DOI: https://doi.org/10.1007/978-3-319-05630-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Hardcover ISBN: 978-3-319-05629-6Published: 04 April 2014
Softcover ISBN: 978-3-319-37965-4Published: 23 August 2016
eBook ISBN: 978-3-319-05630-2Published: 19 March 2014
Edition Number: 1
Number of Pages: XXII, 304
Number of Illustrations: 45 b/w illustrations, 10 illustrations in colour
Topics: Computational Biology/Bioinformatics, Pattern Recognition, Artificial Intelligence, Data Mining and Knowledge Discovery, Imaging / Radiology