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中图法分类号: 文献标识码:A 文章编号:1006-8961(2012)
论文引用格式:陈日新朱明旱。半监督近邻分类方法[J].中国图象图形学报,2012,
半监督近邻分类方法
陈日新,朱明旱
( 湖南文理学院电气与信息工程学院,常德 415000)
摘要:加权KNN(k-Nearest Neighbor)方法,仅利用了k个最近邻训练样本所提供的类别信息,而没考虑测试样本的贡献,因而常会导致一些误判。针对这个缺陷,本文提出了半监督KNN分类方法。该方法对序列样本和非序列样本,均能够较好地执行分类。在分类决策时,还考虑了个最近邻测试样本的贡献,从而提高了分类的正确性。在Cohn-Kanade人脸库上,%,在CMU-AMP人脸库上,%。实验表明,该方法执行效率高,分类效果好。
关键词:加权KNN;贝叶斯理论;半监督KNN;流形
Semi-supervised k-nearest neighbor classification method
Chen Rixing, Zhu Minghan
(College munication and Electric Engineering , Hunan University of Arts and Science, Changde 415000)
Abstract: The category information of the k-nearest neighbor labeled samples is utilized, but the contribution of test samples is omitted in weighted k-nearest neighbor method. So, misclassification often happens. Aimed at the defection, a Semi-supervised k-nearest neighbor method is proposed in this paper. The method can classify sequential samples and non-sequential samples better than k-nearest neighbor. In the decision process of classification, the information of c-nearest neighbor samples in the test set is used. So, classification accuracy is improved. The recognition accuracy of the method is % higher for sequential images in Cohn-Kanade face database, and % higher for non-sequential images in Cohn-Kanade face database than it of weighted k-nearest neighbor method. The expe
论文引用格式:陈日新朱明旱。半监督近邻分类方法[J].中国图象图形学报,2012,
半监督近邻分类方法
陈日新,朱明旱
( 湖南文理学院电气与信息工程学院,常德 415000)
摘要:加权KNN(k-Nearest Neighbor)方法,仅利用了k个最近邻训练样本所提供的类别信息,而没考虑测试样本的贡献,因而常会导致一些误判。针对这个缺陷,本文提出了半监督KNN分类方法。该方法对序列样本和非序列样本,均能够较好地执行分类。在分类决策时,还考虑了个最近邻测试样本的贡献,从而提高了分类的正确性。在Cohn-Kanade人脸库上,%,在CMU-AMP人脸库上,%。实验表明,该方法执行效率高,分类效果好。
关键词:加权KNN;贝叶斯理论;半监督KNN;流形
Semi-supervised k-nearest neighbor classification method
Chen Rixing, Zhu Minghan
(College munication and Electric Engineering , Hunan University of Arts and Science, Changde 415000)
Abstract: The category information of the k-nearest neighbor labeled samples is utilized, but the contribution of test samples is omitted in weighted k-nearest neighbor method. So, misclassification often happens. Aimed at the defection, a Semi-supervised k-nearest neighbor method is proposed in this paper. The method can classify sequential samples and non-sequential samples better than k-nearest neighbor. In the decision process of classification, the information of c-nearest neighbor samples in the test set is used. So, classification accuracy is improved. The recognition accuracy of the method is % higher for sequential images in Cohn-Kanade face database, and % higher for non-sequential images in Cohn-Kanade face database than it of weighted k-nearest neighbor method. The expe
一种动态的加权knn方法 - 中国图象图形学报 来自淘豆网www.taodocs.com转载请标明出处.