  The AdaBoost algorithm has the superiority of resisting overfitting.
Understanding the mysteries of this phenomena is a very fascinating fundamental
theoretical problem. Many studies are devoted to explaining it from statistical
view and margin theory. In this paper, we illustrate it from feature learning
viewpoint, and propose the AdaBoost+SVM algorithm, which can explain the
resistant to overfitting of AdaBoost directly and easily to understand.
Firstly, we adopt the AdaBoost algorithm to learn the base classifiers. Then,
instead of directly weighted combination the base classifiers, we regard them
as features and input them to SVM classifier. With this, the new coefficient
and bias can be obtained, which can be used to construct the final classifier.
We explain the rationality of this and illustrate the theorem that when the
dimension of these features increases, the performance of SVM would not be
worse, which can explain the resistant to overfitting of AdaBoost.
