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  Robust Ensemble Learning for Data Mining

Rätsch, G., Schölkopf, B., Smola, A., Mika, S., Onoda, T., & Müller, K.-R. (2000). Robust Ensemble Learning for Data Mining. In T. Terano, H. Liu, & A. Chen (Eds.), Knowledge Discovery and Data Mining. Current Issues and New Applications: 4th Pacific-Asia Conference, PAKDD 2000, Kyoto, Japan, April 18–20, 2000 (pp. 341-344). Berlin, Germany: Springer. doi:10.1007/3-540-45571-X_39.

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Rätsch, G, Author              
Schölkopf, B1, Author              
Smola, AJ, Author              
Mika, S, Author
Onoda, T, Author
Müller, K-R, Author              
Terano, H., Editor
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1External Organizations, ou_persistent22              

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 Abstract: We propose a new boosting algorithm which similarly 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.

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 Dates: 2003-032000-04
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: 1835
DOI: 10.1007/3-540-45571-X_39
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Title: Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000)
Place of Event: Kyoto, Japan
Start-/End Date: 2000-04-18 - 2000-04-20

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Title: Knowledge Discovery and Data Mining. Current Issues and New Applications: 4th Pacific-Asia Conference, PAKDD 2000, Kyoto, Japan, April 18–20, 2000
Source Genre: Proceedings
 Creator(s):
Terano, T, Editor
Liu, H, Editor
Chen, ALP, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 341 - 344 Identifier: ISBN: 978-3-540-67382-8

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 1805 Sequence Number: - Start / End Page: - Identifier: -