Boosting of Support Vector Machines with Application to Editing
Paula Rangel, Fernando Lozano, E. Garcia · 2006
In this paper, we present a weakened variation of support vector machines that can be used together with Adaboost. Our modified support vector machine algorithm has the following interesting properties: first, it is able to handle distributions over the training data; second, it is a weak algorithm in the sense that it ensures an empirical error upper bounded by 1/2 . Third, when used together with Adaboost, the resulting algorithm is faster than the usual SVM training algorithm; and finally, we show that our boosted SVM can be effective as an editing algorithm.