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.
This paper is a short version of [8].
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Rätsch, G., Schölkopf, B., Smola, A.J., Mika, S., Onoda, T., Müller, KR. (2000). Robust Ensemble Learning for Data Mining. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_39
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DOI: https://doi.org/10.1007/3-540-45571-X_39
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