| 支持向量机增量学习算法综述 |
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| 引用本文： | 李祥纳,艾青,秦玉平,刘卫江. 支持向量机增量学习算法综述[J]. 渤海大学学报(自然科学版), 2007, 28(2): 187-189 |
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| 作者姓名： | 李祥纳 艾青 秦玉平 刘卫江 |
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| 作者单位： | 渤海大学,信息科学与工程学院,辽宁,锦州,121013;辽宁科技大学,计算机科学与工程学院,辽宁,鞍山,114051;东南科学与技术学科博士后流动站,江苏,南京,210096 |
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| 基金项目： | 国家重点基础研究发展计划(973计划);国家自然科学基金 |
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| 摘 要： | 支持向量机增量学习算法，有效的解决了因数据集庞大而引起的内存不足问题，改善了因出现新样本而造成原分类器分类精度降低、分类时间延长的局面。本文阐述了几种具有代表性的增量学习算法，比较了它们的优缺点，给出了进一步的研究方向。
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| 关 键 词： | 支持向量机 增量学习 算法 |
| 文章编号： | 1673-0569（2007）02-0187-03 |
| 修稿时间： | 2007-03-13 |
Summary of SVM incermental learning algorithm |
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| LI Xiang-na,AI Qing,QIN Yu-ping,LIU Wei-jiang. Summary of SVM incermental learning algorithm[J]. Journal of Bohai University:Natural Science Editio, 2007, 28(2): 187-189 |
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| Authors: | LI Xiang-na AI Qing QIN Yu-ping LIU Wei-jiang |
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| Affiliation: | 1, College of Information Science and Engineering, Bohai University, Jinzhou 121013 ,China; 2. College of Computer Science and Engineering,Liaoning University of Science and Technology,Anshan 114051,China ; 3. Post-doctoral Station for Computer Science and Technolgy,Naniing 210096 ,China |
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| Abstract: | SVM incremental learning algorithm effectively solves the problems caused by insufficient memory due to sufficient data sets, and improves the situation in which classification accuracy is lowered due to the appearance of new samples and classification time is extended. A few typical incremental algorithms are provided, their advantages and disadvantages are compared, and further resarch dirction is poointed out. |
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| Keywords: | SVM(Support Vector Machine) incremental learning algorithm |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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