Granular Neural Networks, Pattern Recognition and Bioinformatics (Hardback)

Sankar K. Pal, Shubhra S. Ray, Avatharam Ganivada

Editorial: Springer International Publishing AG, 2017
ISBN 10: 3319571133 / ISBN 13: 9783319571133
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Language: English. Brand new Book. 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. N° de ref. de la librería SPR9783319571133

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Sinopsis: <P>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&NBSP;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&NBSP;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,.</P> <P>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.&NBSP;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,&NBSP;DATA SCIENCE, SYSTEM DESIGN, PATTERN RECOGNITION,&NBSP;IMAGE ANALYSIS,&NBSP;NEURAL COMPUTING, SOCIAL NETWORK ANALYSIS, BIG DATA ANALYTICS, COMPUTATIONAL BIOLOGY AND SOFT COMPUTING.</P>

Reseña del editor:

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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Detalles bibliográficos

Título: Granular Neural Networks, Pattern ...
Editorial: Springer International Publishing AG
Año de publicación: 2017
Encuadernación: Hardback
Condición del libro: New
Edición: 1st ed. 2017.

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Pal, Sankar K.; Ray, Shubhra S.; Ganivada, Avatharam:
Publicado por Cham, Springer International Publishing; 2017. (2017)
ISBN 10: 3319571133 ISBN 13: 9783319571133
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Descripción Cham, Springer International Publishing; 2017., 2017. Studies in Computational Intelligence ; 712. XVIII, 227 p. 23.5 cm x 15.5 cm. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Nº de ref. del artículo: 33318AB

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Ganivada, Avatharam,Ray, Shubhra S.,Pal, Sankar K.
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Descripción Springer, 2017. Hardcover. Condición: Good. Item may show signs of shelf wear. Pages may include limited notes and highlighting. May include supplemental or companion materials if applicable. Access codes may or may not work. Connecting readers since 1972. Customer service is our top priority. Nº de ref. del artículo: mon0001154067

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Pal, Sankar K., Ray, Shubhra S., Ganivad
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Descripción Springer, 2019. Hardcover. Condición: New. Never used! This item is printed on demand. Nº de ref. del artículo: P113319571133

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Sankar K. Pal, Shubhra S. Ray, Avatharam Ganivada
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Descripción Springer 2017-05-20, 2017. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 4-5 working days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely. Nº de ref. del artículo: NU-LBR-02025950

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Sankar K Pal (author), Shubhra S Ray (author), Avatharam Ganivada (author)
Publicado por Springer International Publishing 2017-05-10, Cham (2017)
ISBN 10: 3319571133 ISBN 13: 9783319571133
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Descripción Springer International Publishing 2017-05-10, Cham, 2017. hardback. Condición: New. Nº de ref. del artículo: 9783319571133

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Sankar K. Pal
Publicado por Springer-Verlag Gmbh Mai 2017 (2017)
ISBN 10: 3319571133 ISBN 13: 9783319571133
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Descripción Springer-Verlag Gmbh Mai 2017, 2017. Buch. Condición: Neu. Neuware - 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. 227 pp. Englisch. Nº de ref. del artículo: 9783319571133

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Sankar K. Pal
Publicado por Springer-Verlag Gmbh Mai 2017 (2017)
ISBN 10: 3319571133 ISBN 13: 9783319571133
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AHA-BUCH GmbH
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Descripción Springer-Verlag Gmbh Mai 2017, 2017. Buch. Condición: Neu. Neuware - 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. 227 pp. Englisch. Nº de ref. del artículo: 9783319571133

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Sankar K. Pal
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BuchWeltWeit Inh. Ludwig Meier e.K.
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Descripción Springer-Verlag Gmbh Mai 2017, 2017. Buch. Condición: Neu. Neuware - 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. 227 pp. Englisch. Nº de ref. del artículo: 9783319571133

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Pal, Sankar K.;ray, Shubhra S.;ganivada, Avatharam
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Pal, Sankar K.;ray, Shubhra S.;ganivada, Avatharam
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