Abstract
Parameter tuning for kernels affects the generalization ability of support vector machine (SVM). Although the cross validation method is widely applied to this aim, it is usually time consuming. This paper applies ensemble learning using both Bagging and Boosting to parameter tuning in SVM. It will be shown that the proposed method is effective in particular for large scale data sets and for imbalanced data sets.
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