2000 | OriginalPaper | Buchkapitel
Robust Ensemble Learning for Data Mining
verfasst von : Gunnar Rätsch, Bernhard Schölkopf, Alexander Johannes Smola, Sebastian Mika, Takashi Onoda, Klaus-Robert Müller
Erschienen in: Knowledge Discovery and Data Mining. Current Issues and New Applications
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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.