Description
Book SynopsisResearchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications.The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.
Table of ContentsIntroduction to Pattern Classification; Fundamentals of Ensemble Learning; Combining Classifiers; Popular Ensemble Methods; Modular Approach; Ensemble Diversity; Pruning Ensembles; Evaluation of Ensemble Methods.
