Robust Ensemble Learning for Data Mining
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.