基于模糊c-均值聚类的SVC迭代训练算法

A SVC iterative learning algorithm based on fuzzy c-means clustering

针对支撑向量机(Support vector machine,SVM)在大规模数据的问题,提出了一种基于模糊c-均值聚类样本选择策略的SVC(SVM for classification)迭代训练算法,从样本抽取、迭代训练两个方面进行了改进,并在多个较大规模UCI标准测试集上进行了试验.结果表明,所提出的迭代训练算法收敛快,在保证学习精度的同时使训练速度加倍、支撑向量减少一半.

Focusing on an effective and efficient Support Vector Machine（SVM） classification training algorithm for large samples,a SVC（SVM for classification）iterative learning algorithm based on fuzzy c-means clustering of sample selection strategy was prompted,improved in sample selection iterative training.Experiments on several large-scale UCI data sets showed that,this algorithm could converge quickly with double training speed and cut down the number of support vectors by a half losing quite little accuracy.