| 基于SVM学习方法的分析 |
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| 引用本文： | 卢家胜,王翠平.基于SVM学习方法的分析[J].鲁东大学学报,2006,22(2):105-108. |
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| 作者姓名： | 卢家胜 王翠平 |
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| 作者单位： | 日照广播电视大学开放教育学院 山东日照276826(卢家胜)，山东师范大学信息科学与工程学院 济南250014(王翠平) |
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| 摘 要： | 介绍了增量学习算法、序列最小优化算法、加权支持向量机算法等几种应用于大型数据库,在加快训练速度、降低分类错误率等方面有改进的SVM流行算法.在分析各种算法优缺点的基础上,提出了在线性样本训练、超大规模样本下满足KKT条件的算法是SVM算法的发展方向的观点.
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| 关 键 词： | 支持向量机 增量学习算法 序列最小优化算法 加权支持向量机算法 |
| 文章编号： | 1004-4930(2006)02-0105-04 |
| 修稿时间： | 2005年10月17 |
Analysis of Studying Method Based on SVM |
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| LU Jia-sheng,WANG Cui-ping.Analysis of Studying Method Based on SVM[J].Ludong University Journal (Natural Science Edition),2006,22(2):105-108. |
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| Authors: | LU Jia-sheng WANG Cui-ping |
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| Abstract: | Several kinds of improved support vector machine(SVM) algorithm such as increment learning algorithm,SMO,weighted support vector machine algorithm applied to large scale databases are introduced,to speed up the rate of exercise and to lower the radio of classification mistakes etc are analyzed.Based on the advantages and disadvantages of algorithm,it is pointed out that the development orientation of SVM is to satisfy the algorithm with KKT and with online sample exercise and super large scale databases. |
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| Keywords: | support vector machine increment learning algorithm SMO weighted support vector machine algorithm |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
