Robust ensemble learning for data mining

Gunnar Rätsch, Bernhard Schölkopf, Alexander Johannes Smola, Sebastian Mika, Takashi Onoda, Klaus Muller

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

9 Citations (Scopus)

Abstract

We propose a new boosting algorithm which similaxly to v-Support-Vector Classification allows for the possibility of a pre-specified fraction v of points to lie in the margin area or even on the wrong side of the decision boundary. It gives a nicely interpretable way of controlling the trade-off between minimizing training error and capacity. Furthermore, it can act as a filter for finding and selecting informative patterns from a database.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages341-344
Number of pages4
Volume1805
ISBN (Print)3540673822, 9783540673828
Publication statusPublished - 2000
Externally publishedYes
Event4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 - Kyoto, Japan
Duration: 2000 Apr 182000 Apr 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1805
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000
CountryJapan
CityKyoto
Period00/4/1800/4/20

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Rätsch, G., Schölkopf, B., Smola, A. J., Mika, S., Onoda, T., & Muller, K. (2000). Robust ensemble learning for data mining. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1805, pp. 341-344). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1805). Springer Verlag.