東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Granular neural networks, pattern re...

Pal, Sankar K.

FindBook

Google Book

Amazon

博客來

Granular neural networks, pattern recognition and bioinformatics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Granular neural networks, pattern recognition and bioinformatics/ by 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 International Publishing : : 2017.,
面頁冊數:	xix, 227 p. :ill. (some col.), digital ;24 cm.
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.
Contained By:	Springer eBooks
標題:	Neural networks (Computer science) -
電子資源:	http://dx.doi.org/10.1007/978-3-319-57115-7
ISBN:	9783319571157

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.712..

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

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

532070
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Granular neural networks, pattern recognition and bioinformatics
LDR:02651nmm a2200325 a 4500 001 2100510
003 DE-He213
005 20171226162202.0
006 m d
007 cr nn 008maaau
008 180119s2017 gw s 0 eng d
020 $a 9783319571157 $q (electronic bk.)
020 $a 9783319571133 $q (paper)
024 7 $a 10.1007/978-3-319-57115-7 $2 doi
035 $a 978-3-319-57115-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .P153 2017
100 1 $a Pal, Sankar K. $3 577802
245 1 0 $a Granular neural networks, pattern recognition and bioinformatics $h [electronic resource] / $c by Sankar K. Pal, Shubhra S. Ray, Avatharam Ganivada.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xix, 227 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.712
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.
650 0 $a Neural networks (Computer science) $3 532070
650 0 $a Pattern recognition systems. $3 527885
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Computational Biology/Bioinformatics. $3 898313
700 1 $a Ray, Shubhra S. $3 3242251
700 1 $a Ganivada, Avatharam. $3 3242252
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v.712. $3 3242253
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-57115-7
950 $a Engineering (Springer-11647)