一种基于AdaBoost的SVM分类器

针对AdaBoost的分量分类器的分类精度和差异性互为矛盾、以至于该矛盾的存在降低了AdaBoost算法的分类精度和泛化性的问题,提出了一种变σ-AdaBoostRBFSVM算法,通过根据训练样本调整各个分量分类器的核函数参数值,使分量分类器在精度和差异性之间达到一定的平衡,从而提高了集成分类器的分类精度和泛化性。对标准数据集的分类实验结果表明了算法的有效性。

A Combined SVM Classifier Based on AdaBoost

The relation between the performance of AdaBoost and that of component classifiers is analyzed, and the approach of improving the classification performance of RBFSVM is studied. There is an inconsistency between the accuracy and the diversity of component classifiers, and the inconsistency affects the generalization performance of the algorithm. A new variable ó - AdaBoostSVM is proposed by adjusting the kernel function parameter of the component classifier based on the distribution of training samples, and it improves the classification performance by making a balance between the accuracy and diversity of component classifiers. Experimental results indicate the effectiveness of the proposed algorithm.