摘要
构造了一种空间支持向量域分类器(SSVDC).在训练阶段分别对正负两类样本进行支持向量域描述,根据描述边界将数据空间划分为互不相交区域,并设定相应的分类准则.在测试阶段,分别计算待识别样本与两个最小包围超球球心的距离,根据其与超球半径的大小关系确定待识别样本所处区域,并采取相应分类准则完成分类.UCI数据集上的多个数值实验表明,与支持向量机(SVM),支持向量域分类器(SVDC)相比,SSVDC具有好的鲁棒性,训练时间可缩短为SVM的20.6%,分类精度比SVDC提高45.9%.
A space support vector domain classifier （SSVDC） is proposed. In the training process, the support vector domain description （SVDD） is applied to both the positive and negative classes, disconnect regions are obtained according to the description boundaries and different classification rules are erected for the corresponding regions. In the test phase, the distances from the test sample to each hypersphere centers are computed, the region that the test samples belong to is confirmed according to the relations between their central distances and the hyperspheres radii, so that corresponding rules can be adopted. Numerical experiments on UCI data show that compared with existing algorithms SVM and SVDC, SSVDC has better robustness, a shorter training time of about 20. 6% SVM and a classification accuracy which is about 45.9% higher than that of SVDC in the best ease.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2008年第6期1080-1083,1088,共5页
Journal of Xidian University
基金
国家自然科学基金资助(60674108
60574075
60705004)
