关键词: “有效”候选集, 挖心, 过边界, 支持向量机

Abstract: Sample selection is an important issue for Support Vector Machines（SVMs）.But，at present most sample selection methods have a common disadvantage that the candidate set for training sample is the whole sample space，so，it may select the interior samples or “outliers” that have little or even bad effect on the classifying quality.So，two improved methods based on effective candidate set are proposed in the paper.By using these two methods，the effective candidate set is identified through “removing center”and eliminating the“outliners”，and then training samples in this effective candidate set are selected.The experimental results show that the methods reserve effective candidate samples undoubtedly，and also improve the performance of the SVM classifiers.

Key words: effective candidate set, removing center, outliers, Support Vector Machines（SVMs）