一种基于聚类的快速局部支持向量机算法  

An algorithm of fast local support vector machine based on clustering

作　　者：浩庆波 牟少敏[1,2] 尹传环[3] 昌腾腾 崔文斌[1] 

机构地区：[1]山东农业大学信息科学与工程学院,山东泰安271018 [2]山东农业大学农业大数据研究中心,山东泰安271018 [3]北京交通大学计算机与信息技术学院,北京100044

出　　处：《山东大学学报（工学版）》2015年第1期13-18,共6页Journal of Shandong University（Engineering Science）

基　　金：山东省自然科学基金资助项目(ZR2012FM024);国家自然科学青年基金资助项目(61105056);山东省农业重大应用技术创新课题资助项目

摘　　要：为进一步改善局部支持向量机的分类效率和分类精度,提出一种改进的局部支持向量机算法。该算法对每类训练样本分别进行聚类,使用聚类生成的样本中心点集代替样本,使用改进的k最近邻算法选取测试样本的k个近邻。分别在UCI数据集和自建树皮图像数据集上对本研究算法的有效性进行测试。实验结果表明,本研究提出的算法在分类精度和效率上具有一定的优势。In order to further improve the classification efficiency and precision of local support vector machine,a new al-gorithm was proposed.The two major improvements were as follows.First,every type of training samples was clustered seperately,and the training samples were substituted for sample centers generated by clustering.Second,the k nearest neighbors of test samples were selected by using the improved k-nearest neighbor algorithm.Tests were done on UCI data sets and bark image data sets made by the proposed algorithm to verify its effectiveness.Experimental results demonstrated that this algorithm had certain superiority of classification accuracy and efficiency.

关 键 词：局部支持向量机 k最近邻 K均值聚类 核函数 分类 纹理特征 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]