摘要
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 nonlinear 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.