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Granular neural networks, pattern re...

Ganivada, Avatharam.

Granular neural networks, pattern recognition and bioinformatics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Granular neural networks, pattern recognition and bioinformaticsby Sankar K. Pal, Shubhra S. Ray, Avatharam Ganivada.
Author:	Pal, Sankar K.
other author:	Ray, Shubhra S.
Published:	Cham :Springer International Publishing :2017.
Description:	xix, 227 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Neural networks (Computer science)
Online resource:	http://dx.doi.org/10.1007/978-3-319-57115-7
ISBN:	9783319571157$q(electronic bk.)

Granular neural networks, pattern recognition and bioinformatics
Pal, Sankar K.

Granular neural networks, pattern recognition and bioinformatics[electronic resource] /by Sankar K. Pal, Shubhra S. Ray, Avatharam Ganivada. - Cham :Springer International Publishing :2017. - xix, 227 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.7121860-949X ;. - Studies in computational intelligence ;v. 216..

Introduction to Granular Computing, Pattern Recognition and Data Mining -- Classiﬁcation using Fuzzy Rough Granular Neural Networks -- Clustering using Fuzzy Rough Granular Self-Organizing Map -- Fuzzy Rough Granular Neural Network and Unsupervised Feature Selection.

This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinformatics applications. The book is recommended for both students and practitioners working in computer science, electrical engineering, data science, system design, pattern recognition, image analysis, neural computing, social network analysis, big data analytics, computational biology and soft computing.

ISBN: 9783319571157$q(electronic bk.)

Standard No.: 10.1007/978-3-319-57115-7doiSubjects--Topical Terms:

181982
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Granular neural networks, pattern recognition and bioinformatics
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245 1 0 $a Granular neural networks, pattern recognition and bioinformatics $h [electronic resource] / $c by Sankar K. Pal, Shubhra S. Ray, Avatharam Ganivada.
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505 0 $a Introduction to Granular Computing, Pattern Recognition and Data Mining -- Classiﬁcation using Fuzzy Rough Granular Neural Networks -- Clustering using Fuzzy Rough Granular Self-Organizing Map -- Fuzzy Rough Granular Neural Network and Unsupervised Feature Selection.
520 $a This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinformatics applications. The book is recommended for both students and practitioners working in computer science, electrical engineering, data science, system design, pattern recognition, image analysis, neural computing, social network analysis, big data analytics, computational biology and soft computing.
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