摘要
提出了一种基于β因子历史样本淘汰机制的在线学习算法.对UCI标准数据集中的部分样本集的测试结果表明:该机制有效地淘汰了一些样本,在保持了分类精度和泛化能力的情况下,大大加快了增量学习的训练速度.
A new online learning algorithm was proposed in this paper, which was based on the β factor of dis-carding history sample. Several standard benchmark data sets in the UCI were tested through the new online learning algorithm. The results showed that some samples could be discarded effectively through β factor, which leaded to fast incremental learning with no influence on the precision of training and generalization.
出处
《仲恺农业技术学院学报》
2007年第2期31-35,共5页
Journal of Zhongkai Agrotechnical College
关键词
支持向量机
在线学习
增量学习
support vector machine
online learning
incremental learning
