Emerging Paradigms in Machine Learning

Sheela Ramanna

ISBN 10: 3642286984 / ISBN 13: 9783642286988

Published by Springer, J.B. Metzler Jul 2012, 2012

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 520 pp. Englisch.

Seller Inventory # 9783642286988

Bibliographic Details

• Title: Emerging Paradigms in Machine Learning
• Publisher: Springer, J.B. Metzler Jul 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 3642286984
• ISBN 13: 9783642286988
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 33D millimeters 241 millimeters width by 160 millimeters height by 33 millimeters depth
• Weight: 939 grams

About this title

Synopsis

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.