摘要
支持向量机(SVM)花费大量时间用于对非支持向量样本的优化.根据支持向量都是位于两类边界的靠近分类超平面的样本点,本文提出首先利用基于中心距离比值法排除大部分远离分类超平面的样本,然后以最小类间距离样本数作为测度进一步选择边界样本,得到包含所有支持向量的最小样本集,构成新的训练样本集训练SVM.将提出的算法应用于解决医学图像奇异点检测问题.实验结果表明,该算法减小了训练样本集的规模,有效地缩短了SVM训练算法的时间,同时获得了较高的检出率.
Support vector machine（SVM） takes huge time for optimization of samples of unsupported vectors. And this makes it time-consuming to train supported vector machine classifier. The supported vectors are located near the region of hyper-plane of two classes. Based on ratio of distance between the centers of two classes,a cropping method to get a smaller training set was first repre- sented. Moreover,the least distance between two classes was considered and the sample set was cropped. The sample set selected at last not only was smallest, but also covered all supported vectors. The proposed algorithm was used to solve the detection problem of image bizarre points. The results of experiments showed that the size of training set and abbreviates time of training stage could be reduced efficiently. At the same time,it gains a higher detection rate than the method based on SMO was achieved.
出处
《厦门大学学报（自然科学版）》
CAS
CSCD
北大核心
2007年第4期506-509,共4页
Journal of Xiamen University：Natural Science
基金
航空科学基金(05F07001)资助
关键词
支持向量机
训练算法
修剪算法
微钙化点检测
support vector machine
training algorithm
cropping algorithm
microcalcification detection
