Creating an ensemble of diverse support vector machines using adaboost
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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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. Finally, we show that our boosted SVM can be effective as an editing algorithm.