Emerging Paradigms in Machine Learning
Language: English
Published by Springer Berlin Heidelberg, 2012
Series: Book 22 of 235 - Smart Innovation, Systems and Technologies
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 94.47
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Seller Inventory # 9783642286988
- Title
- Emerging Paradigms in Machine Learning
- Author
- Sheela Ramanna
- Publisher
- Springer Berlin Heidelberg
- Publication year
- 2012
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3642286984
- ISBN 13
- 9783642286988
- Item weight
- 939 grams
- Dimensions
- 241x160x33 mm
- Series
- Book 22 of 235: Smart Innovation, Systems and Technologies
This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.
"Synopsis" may belong to another edition of this title.
From the Back Cover
This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.
"About the title" may belong to another edition of this title.
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