| 一种新的SVM对等增量学习算法 |
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| 引用本文： | 王晓丹,郑春颖,吴崇明,张宏达.一种新的SVM对等增量学习算法[J].计算机应用,2006,26(10):2440-2443. |
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| 作者姓名： | 王晓丹 郑春颖 吴崇明 张宏达 |
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| 作者单位： | 空军工程大学,导弹学院,陕西,三原,713800 |
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| 基金项目： | 国家自然科学基金;陕西省自然科学基金 |
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| 摘 要： | 在分析支持向量机（SVM）寻优问题的KKT条件和样本分布之间关系的基础上，分析了新增样本的加入对SV集的影响，定义了广义KKT条件。基于原训练样本集和新增训练样本集在增量训练中地位等同，提出了一种新的SVM增量学习算法。算法在及时淘汰对后继分类影响不大的样本的同时保留了含有重要分类信息的样本。对标准数据集的实验结果表明，算法获得了较好的性能。
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| 关 键 词： | 支持向量机 增量学习 |
| 文章编号： | 1001-9081（2006）10-2440-04 |
| 收稿时间： | 2006-04-10 |
| 修稿时间： | 2006-04-102006-06-27 |
New algorithm for SVM-Based incremental learning |
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| WANG Xiao-dan,ZHENG Chun-ying,WU Chong-ming,ZHANG Hong-da.New algorithm for SVM-Based incremental learning[J].journal of Computer Applications,2006,26(10):2440-2443. |
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| Authors: | WANG Xiao-dan ZHENG Chun-ying WU Chong-ming ZHANG Hong-da |
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| Affiliation: | Missile Institute, Air Force Engineering University, Sanyuan Shaanxi 713800, China |
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| Abstract: | Based on the analysis of the relation between the Karush-Kuhn-Tucker (KKT) conditions of Support Vector Machine(SVM) and the distribution of the training samples, the possible changes of support vector set after new samples are added to training set were analyzed, and the generalized Karush-Kuhn-Tucker conditions were defined. Based on the equivalence between the original training set and the newly added training set, a new algorithm for SVM-based incremental learning was proposed. With this algorithm, the useless samples were discarded and the useful training samples of importance were reserved. Experimental results with the standard dataset indicate the effectiveness of the proposed algorithm. |
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| Keywords: | Support Vector Machine(SVM) incremental learning |
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