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Emerging Paradigms in Machine Learning

Springer Berlin,
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Covering state-of-the-art and emerging paradigms in machine learning, and featuring a number of real-world applications, this book also presents key topics and algorithms that form the core of machine learning (ML) research, including granular computing.


Titel: Emerging Paradigms in Machine Learning
Autoren/Herausgeber: Sheela Ramanna, Lakhmi C. Jain, Robert J. Howlett (Hrsg.)
Aus der Reihe: Smart Innovation, Systems and Technologies
Ausgabe: 2013

ISBN/EAN: 9783642435744

Seitenzahl: 498
Format: 23,5 x 15,5 cm
Produktform: Taschenbuch/Softcover
Gewicht: 789 g
Sprache: Englisch

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. - Newsletter
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