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.
