Pattern Classification Using Ensemble Methods


Researchers 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.

Sample Chapter(s)
Chapter 1: Introduction to Pattern Classification (246 KB)

  • Introduction to Pattern Classification
  • Introduction to Ensemble Learning
  • Ensemble Classification
  • Ensemble Diversity
  • Ensemble Selection
  • Error Correcting Output Codes
  • Evaluating Ensembles of Classifiers

Readership: Researchers, advanced undergraduate and graduate students in machine learning and pattern recognition.
  • World Scientific Publishing Company; November 2009
  • ISBN 9789814468312
  • Read online, or download in secure PDF format
  • Title: Pattern Classification Using Ensemble Methods
  • Author: Lior Rokach
  • Imprint: WSPC