Some results on weakly accurate base learners for boosting regression and classification

Wenxin Jiang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

One basic property of the boosting algorithmis its ability to reduce the training error, subject to the critical assumption that the base learners generate 'weak' (or more appropriately, 'weakly accurate') hypotheses that are better that randomguessing. We exploit analogies between regression and classification to give a characterization on what base learners generate weak hypotheses, by introducing a geometric concept called the angular span for the base hypothesis space. The exponential convergence rates of boosting algorithms are shown to be bounded below by essentially the angular spans. Sufficient conditions for nonzero angular span are also given and validated for a wide class of regression and classification systems.

Original languageEnglish (US)
Title of host publicationMultiple Classifier Systems - First International Workshop, MCS 2000, Proceedings
Pages87-96
Number of pages10
StatePublished - Dec 1 2000
Event1st International Workshop on Multiple Classifier Systems, MCS 2000 - Cagliari, Italy
Duration: Jun 21 2000Jun 23 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1857 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Multiple Classifier Systems, MCS 2000
CountryItaly
CityCagliari
Period6/21/006/23/00

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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