Robust Ensemble Learning for Data Mining

Authors:
Gunnar Rätsch;Bernhard Schölkopf;Alex J. Smola;Sebastian Mika;Takashi Onoda;Klaus-Robert Müller
Affiliations:
-;-;-;-;-;-
Venue:
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Year:
2000

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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.