An AdaBoost Algorithm with SVM Based on Nonlinear Decision Function

This paper presents a method of using nonlinear decision function to improve the performance of AdaBoost with SVM based weak learners. Compared with the existing AdaBoostSVM methods, this method, named ERBF-AdaBoostSVM, has advantages of higher hate rate and better generalization performance. This method also provides non-linear separator in the weak learner space and classifies accurately more examples. Experimental results demonstrated that ERBF-AdaBoostSVM achieve better generalization performance and higher hate rate than the existing SVM and AdaBoostSVM methods.