Abstract: A combined classification algorithm based on improved AdaBoost and Support Vector Machine，is proposed in order to deal with the problems of multiclass classification.Adopt a rule sampling to solve the unbalance of samples in the SVM.Improving the AdaBoost makes it consider the importance of sparse sample distribution at the beginning，this is advantageous to the right demarcation of rare sample.Experiment proves this algorithm can raise the generalization ability compared with the standard SVM.

Key words: AdaBoost, Support Vector Machine（SVM）, combined classification, rule sampling

摘要： 提出了一种改进的AdaBoost算法与支持向量机组合的分类方法，用来处理多类别分类。采用规则抽样来解决支持向量机分类中正负样本的不平衡性，改进AdaBoost算法，使其在初始化时考虑样本分布稀疏的重要性，有利于稀有类样本的正确划分。实验结果表明，此方法与标准支持向量机分类器相比，泛化性能有一定程度的提高。

关键词: AdaBoost, 支持向量机, 组合分类器, 规则抽样