Robust Ensemble Learning for Data Mining

Rätsch, G; Schölkopf, B; Smola, AJ; Mika, S; Onoda, T; Müller, K-R; Terano, H.

doi:10.1007/3-540-45571-X_39

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Robust Ensemble Learning for Data Mining

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

https://link.springer.com/content/pdf/10.1007%2F3-540-45571-X_39.pdf
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E5C9-8

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.