Title
Emerging Paradigms in Machine Learning [electronic resource] / edited by Sheela Ramanna, Lakhmi C Jain, Robert J. Howlett.
Publication
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Physical Description
1 online resource (XXII, 498 p).
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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. .
Variant and related titles
Springer ENIN.
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Added to Catalog
April 11, 2019
Series
Smart Innovation, Systems and Technologies, 13
Contents
From the content: Emerging Paradigms in Machine Learning: An Introduction
Extensions of Dynamic Programming as a New Tool for Decision Tree Optimization
Optimised information abstraction in granular Min/Max clustering
Mining Incomplete Data—A Rough Set Approach
Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigation.
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