计算机科学 ›› 2016, Vol. 43 ›› Issue (5): 230-233.doi: 10.11896/j.issn.1002-137X.2016.05.042
王敏光,王喆
WANG Min-guang and WANG Zhe
摘要： 针对传统的支持向量数据描述模型忽略了样本分布的重要性,提出了基于类心距离的模糊支持向量数据描述算法,并将其应用在UCI机器学习数据库的二分类和多分类数据集中。该算法利用样本到两类中心距离的比值赋予样本权重,增大贡献度大的样本的权重,降低贡献度小的样本的权重,突出样本之间的差异性,从而提高了算法的分类效果。实验表明,该算法具有比传统支持向量数据描述更好的学习能力和分类效果。
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