Improving Svm Learning Accuracy with Adaboost

Support vector machine (SVM) is based on the VC theory and the principle of structural risk minimization. For some learning domains that need more accurate learning performance, SVM can be improved for this objective. This paper describes an algorithm - Boost-SVM, which puts SVM into AdaBoost framework to improve the learning accuracy of the SVM algorithm. By changing the weights of the training examples in the re-sampling process of AdaBoost, SVM appears to be more accurate. The experimental results show that the proposed method has a competitive learning ability and acquires better accuracy than SVM.