摘要
提出一种新的支持向量机增量学习算法 .分析了新样本加入训练集后 ,支持向量集的变化情况 .基于分析结论提出新的学习算法 .算法舍弃对最终结论无用的样本 ,使得学习对象的知识得到了积累 .实验结果表明本算法在保证分类准确度的同时 。
This paper presents a novel approach to incremental support vector machine (SVM) learning algorithm. It analyses the possible change of support vector set after new samples are added to training set. Based on the analysis result, a novel algorithm is presented. In this algorithm useless sample is discarded and knowledge is accumulated. The experiment result shows that this algorithm is more effective than traditional SVM while the classification precision is also guaranteed.
出处
《厦门大学学报（自然科学版）》
CAS
CSCD
北大核心
2002年第6期687-691,共5页
Journal of Xiamen University：Natural Science
基金
工业控制技术国家重点开放实验室资助项目(K0 10 0 1)
