Explaining adaboost

Research output: Chapter in Book/Report/Conference proceedingChapter

140 Scopus citations

Abstract

Boosting is an approach to machine learning based on the idea of creating a highly accurate prediction rule by combining many relatively weak and inaccurate rules. The AdaBoost algorithm of Freund and Schapire was the first practical boosting algorithm, and remains one of the most widely used and studied, with applications in numerous fields. This chapter aims to review some of the many perspectives and analyses of AdaBoost that have been applied to explain or understand it as a learning method, with comparisons of both the strengths and weaknesses of the various approaches.

Original languageEnglish (US)
Title of host publicationEmpirical Inference
Subtitle of host publicationFestschrift in Honor of Vladimir N. Vapnik
PublisherSpringer Berlin Heidelberg
Pages37-52
Number of pages16
ISBN (Electronic)9783642411366
ISBN (Print)9783642411359
DOIs
StatePublished - Jan 1 2013

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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