Improved Fuzzy Support Vector Machine and Its Applications
Because the sample usually has a lot of vague information, and the sparseness of the samples distribution are different, through studying some of the existing fuzzy support vector machine method, a fuzzy support vector machine method based on fuzzy K neighbors is presented in this paper.In this method, the sample mean is calculated, and the center of each class is got;then the distance between the sample and the center is calculated, according to the distance samples initial membership is got;by finding K neighbors for each sample point, the sample membership degree is calculated according to the fuzzy K nearest neighbor method, then the paper integrates initial membership degree with fuzzy K neighbors membership by a certain percentage that obtains the final membership value of the sample.
Fuzzy Support Vector Machine Membership Fuzzy K Neighbors
Chen Hong-ming Zhang Hui
School of Computer Science Huaiyin Institute of Technology Huaian,China
国际会议
成都
英文
968-972
2011-07-15（万方平台首次上网日期，不代表论文的发表时间）