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Granular neural networks, pattern re...

Ganivada, Avatharam.

Granular neural networks, pattern recognition and bioinformatics /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.3/2
書名/作者:	Granular neural networks, pattern recognition and bioinformatics // Sankar K. Pal, Shubhra S. Ray. Avatharam Ganivada.
作者:	Pal, Sankar K.
其他作者:	Ray, Shubhra S.
出版者:	Cham : : Springer,, c2017.
面頁冊數:	xix, 227 p. : : ill. (some col.) ;; 24 cm.
標題:	Neural networks (Computer science)
標題:	Pattern recognition systems.
ISBN:	9783319571133 (hbk.) :
書目註:	Includes bibliographical references and index.
內容註:	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.
摘要、提要註:	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.

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

Granular neural networks, pattern recognition and bioinformatics /Sankar K. Pal, Shubhra S. Ray. Avatharam Ganivada. - Cham :Springer,c2017. - xix, 227 p. :ill. (some col.) ;24 cm. - Studeies in computational intelligence,v. 7121860-949X ;. - Studies in computational intelligence ;v.379..

Includes bibliographical references and index.

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: 9783319571133 (hbk.) :NTD 4,255

LCCN: 2017937261Subjects--Topical Terms:

386157
Neural networks (Computer science)

LC Class. No.: QA76.87 / .P35 2017

Dewey Class. No.: 006.3/2

Granular neural networks, pattern recognition and bioinformatics /
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245 1 0 $a Granular neural networks, pattern recognition and bioinformatics / $c Sankar K. Pal, Shubhra S. Ray. Avatharam Ganivada.
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300 $a xix, 227 p. : $b ill. (some col.) ; $c 24 cm.
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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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650 0 $a Pattern recognition systems. $3 189561
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