Robust ensemble learning for data mining

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

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

12 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 publicationKnowledge Discovery and Data Mining
Subtitle of host publicationCurrent Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings
EditorsTakao Terano, Huan Liu, Arbee L.P. Chen
PublisherSpringer Verlag
Pages341-344
Number of pages4
ISBN (Print)3540673822, 9783540673828
DOIs
Publication statusPublished - 2000
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)0302-9743
ISSN (Electronic)1611-3349

Other

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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