Scalable Pattern Recognition Algorithms

Applications in Computational Biology and Bioinformatics
Buch | Hardcover
XXII, 304 Seiten
2014 | 2014
Springer International Publishing (Verlag)
978-3-319-05629-6 (ISBN)

Lese- und Medienproben

Scalable Pattern Recognition Algorithms - Pradipta Maji, Sushmita Paul
106,99 inkl. MwSt
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.

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.

Introduction to Pattern Recognition and Bioinformatics.- Part I Classification.- Neural Network Tree for Identification of Splice Junction and Protein Coding Region in DNA.- Design of String Kernel to Predict Protein Functional Sites Using Kernel-Based Classifiers.- Part II Feature Selection.- Rough Sets for Selection of Molecular Descriptors to Predict Biological Activity of Molecules.- f -Information Measures for Selection of Discriminative Genes from Microarray Data.- Identification of Disease Genes Using Gene Expression and Protein-Protein Interaction Data.- Rough Sets for Insilico Identification of Differentially Expressed miRNAs.- Part III Clustering.- Grouping Functionally Similar Genes from Microarray Data Using Rough-Fuzzy Clustering.- Mutual Information Based Supervised Attribute Clustering for Microarray Sample Classification.- Possibilistic Biclustering for Discovering Value-Coherent Overlapping d -Biclusters.- Fuzzy Measures and Weighted Co-Occurrence Matrix for Segmentation of Brain MR Images.

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)

Erscheint lt. Verlag 4.4.2014
Zusatzinfo XXII, 304 p. 55 illus., 10 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 686 g
Themenwelt Informatik Weitere Themen Bioinformatik
Naturwissenschaften Biologie
Schlagworte Artificial Intelligence • Bioinformatics • Computational Biology • Computational Intelligence • Data Mining • machine learning • Medical Imaging • pattern recognition • Soft Computing
ISBN-10 3-319-05629-8 / 3319056298
ISBN-13 978-3-319-05629-6 / 9783319056296
Zustand Neuware
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