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。
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.
出处
《计算机应用》
CSCD
北大核心
2006年第10期2440-2443,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(50505051)
陕西省自然科学研究计划项目(2004F36)
关键词
支持向量机
增量学习
Support Vector Machine（SVM）
incremental learning
