Granular Neural Networks, Pattern Recognition and Bioinformatics
Language: English
Published by Springer, 2017
Series: Book 224 of 538 - Studies in Computational Intelligence
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 147.93
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Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - 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 theformation 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 inefficient 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 forfuture 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.…
Seller Inventory # 9783319571133
- Title
- Granular Neural Networks, Pattern Recognition and Bioinformatics
- Author
- Shubhra S. Ray
- Publisher
- Springer
- Publication year
- 2017
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3319571133
- ISBN 13
- 9783319571133
- Item weight
- 489 grams
- Dimensions
- 241x160x20 mm
- Series
- Book 224 of 538: Studies in Computational Intelligence
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 forfuture 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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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 forfuture 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.
"About the title" may belong to another edition of this title.
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