Emerging Paradigms in Machine Learning

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.

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.

State of the art of emerging paradigms in machine learning including some real world applications Latest research in machine learning and biologically-based techniques for the design and implementation of intelligent systems Written by leading experts in the field

Inhalt
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 DataA Rough Set Approach.- Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigation.