摘要
基于支持向量数据描述和改进的可能性c-均值聚类算法,提出了一种模糊的多类分类学习机。首先通过一个改进的PCM算法来计算每个样本对于每类的权值矩阵,该权值也反映了该样本对某类的重要程度;然后将该权值矩阵应用到支持向量数据描述方法中,并对样本进行训练;最后给出了一个针对多类分类的分类规则(函数),并从理论上证明该分类规则满足贝叶斯优化决策理论。通过对比实验分析,本文提出的算法在分类精度和训练时间上都有较大的改善。
In this paper, a novel fuzzy classifier for multi-classlfication problems, based on Support Vector Data Description （SVDD） and improved PCM, is proposed. The proposed method is the robust version of SVDD by assigning a weight to each data point, which represents fuzzy membership degree of the cluster computed by the improved PCM method. Accordingly, this paper presents the multi classification algorithm based on the robust weighted SVDD, and gives the simple classification rule. Experimental results show that the proposed method can reduce the effect of outliers and yield higher classification rate.
出处
《计算机科学》
CSCD
北大核心
2008年第8期149-153,共5页
Computer Science
基金
国家科技型中小企业技术创新基金(05C26212120357)
