智能系统学报Issue(4)：392-400,9.DOI:10.3969/j.issn.1673-4785.201312040
支持向量机的多观测样本二分类算法
Binary-class classification algorithm with multiple-access acquired objects based on the SVM
摘要
Abstract
The binary-class classification algorithm with multiple-access acquired objects based on the SVM is pro-posed for the purpose of classification of an object given with multiple observations in this paper .In each classifica-tion, initially all single observation samples in the multiple observation sample set are restricted to a same class .Two hypotheses are made for the class of the multiple observation sample set , and the class is determined by comparing the optimal values of the different objective functions under different class hypotheses .This method does not require training the classifier or early feature representation of the training set , instead, it takes advantage of the continuity law of the feature space of similar samples with the labeled samples and multiple observation samples as a whole , making the algorithm more accurate for classifications .Experiments show that the proposed method is valid and effi-cient.关键词
模式识别/多观测/同类样本/SVM/二分类Key words
pattern recognition/multiple observations/similar samples/SVM/binary-class classification分类
信息技术与安全科学引用本文复制引用
李欢,王士同..支持向量机的多观测样本二分类算法[J].智能系统学报,2014,(4):392-400,9.基金项目
国家自然科学基金资助项目（61272210）；江苏省自然科学基金资助项目（ BK2011417， BK2011003）；江苏省“333”工程基金资助项目（ BRA2011142）． （）
