We propose a new boosting algorithm which similaxly 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.
CITATION STYLE
Rätsch, G., Schölkopf, B., Smola, A. J., Mika, S., Onoda, T., & Müller, K. R. (2000). Robust ensemble learning for data mining. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1805, pp. 341–344). Springer Verlag. https://doi.org/10.1007/3-540-45571-x_39
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