计算机应用研究2011，Vol.28Issue(5)：1685-1687,1691,4.DOI:10.3969/j.issn.1001-3695.2011.05.026
基于云模型的最接近支持向量机增量学习方法
Incremental PSVM learning algorithm based on cloud model
摘要
Abstract
Aiming at the limitations of incremental learning in classical SVM, this paper proposed an incremental PSVM ( proximal SVM) learning algorithm based on cloud model.Employed the fast learning ability of PSVM to yield the initial classification hyperplane, and then, reduced all training datasets by using k-NN method and the plane.After that, utilized cloud model to directly discriminate analysis on the reduced dataset.The simple algorithm, with less computational time and better anti-noise ability, could well embody the distribution of incremental samples and could be solve without iteration.Experiment results show that the algorithm can not only keep well classification accuracy and generalization ability, but also improve the training speed.关键词
支持向量机/云模型/分类/增量学习Key words
support vector machines(SVM)/ cloud model/ classification/ incremental learning分类
信息技术与安全科学引用本文复制引用
金珠,马小平..基于云模型的最接近支持向量机增量学习方法[J].计算机应用研究,2011,28(5):1685-1687,1691,4.基金项目
国家自然科学基金资助项目(60974126,60974050) （60974126,60974050）
江苏省自然科学基金资助项目(BK2009094) （BK2009094）
