Robust ensemble learning for data mining

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

doi:10.1007/3-540-45571-x_39

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 proceeding › Conference contribution

10 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 language	English
Title of host publication	Knowledge Discovery and Data Mining
Subtitle of host publication	Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings
Editors	Takao Terano, Huan Liu, Arbee L.P. Chen
Publisher	Springer Verlag
Pages	341-344
Number of pages	4
ISBN (Print)	3540673822, 9783540673828
DOIs	https://doi.org/10.1007/3-540-45571-x_39
Publication status	Published - 2000
Event	4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 - Kyoto, Japan Duration: 2000 Apr 18 → 2000 Apr 20

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1805
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000
Country	Japan
City	Kyoto
Period	00/4/18 → 00/4/20

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/3-540-45571-x_39

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Cite this

Rätsch, G., Schölkopf, B., Smola, A. J., Mika, S., Onoda, T., & Müller, K. R. (2000). Robust ensemble learning for data mining. In T. Terano, H. Liu, & A. L. P. Chen (Eds.), Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings (pp. 341-344). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1805). Springer Verlag. https://doi.org/10.1007/3-540-45571-x_39

Robust ensemble learning for data mining. / Rätsch, Gunnar; Schölkopf, Bernhard; Smola, Alexander Johannes; Mika, Sebastian; Onoda, Takashi; Müller, Klaus Robert.

Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings. ed. / Takao Terano; Huan Liu; Arbee L.P. Chen. Springer Verlag, 2000. p. 341-344 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1805).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Rätsch, G, Schölkopf, B, Smola, AJ, Mika, S, Onoda, T & Müller, KR 2000, Robust ensemble learning for data mining. in T Terano, H Liu & ALP Chen (eds), Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1805, Springer Verlag, pp. 341-344, 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000, Kyoto, Japan, 00/4/18. https://doi.org/10.1007/3-540-45571-x_39

Rätsch G, Schölkopf B, Smola AJ, Mika S, Onoda T, Müller KR. Robust ensemble learning for data mining. In Terano T, Liu H, Chen ALP, editors, Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings. Springer Verlag. 2000. p. 341-344. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-45571-x_39

Rätsch, Gunnar ; Schölkopf, Bernhard ; Smola, Alexander Johannes ; Mika, Sebastian ; Onoda, Takashi ; Müller, Klaus Robert. / Robust ensemble learning for data mining. Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings. editor / Takao Terano ; Huan Liu ; Arbee L.P. Chen. Springer Verlag, 2000. pp. 341-344 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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