Emerging Paradigms in Machine Learning: 13

Sheela Ramanna, Lakhmi C. Jain, Robert J. Howlett (editors)

ISBN 10: 3642286984 / ISBN 13: 9783642286988

Editore: Springer, 2012

Riguardo questo articolo

THERE ARE NO TARIFFS OR CUSTOMS DUTIES ON BOOKS. 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. 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, sheds 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. Hardback. Visible score mark to front and rear boards. Text is clean and tight.

Codice articolo 436780

Dati bibliografici

• Titolo: Emerging Paradigms in Machine Learning: 13
• Editore: Springer
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 3642286984
• ISBN 13: 9783642286988
• Rilegatura: Hardcover
• Condizione: Good

Descrizione articolo

Riassunto

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.