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Emerging Paradigms in Machine Learning

by Sheela Ramanna

Part of the Smart Innovation, Systems and Technologies Series

Emerging Paradigms in Machine Learning 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.

Book Information

ISBN: 9783642286988
Publication date: 31st July 2012
Author: Sheela Ramanna
Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. K an imprint of Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Format: Hardback
Pagination: 498 pages
Categories: Machine learning,

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