摘要
提出了一种新的基于壳向量的增量式支持向量机快速学习算法.在增量学习的过程中,利用训练样本集中的几何信息,在样本中选取一部分最有可能成为支持向量的样本———壳向量,它是支持向量集的一个规模较小的扩展集,将其作为新的训练样本集,再进行支持向量训练.这在很大程度上减少了求取支持向量过程中的二次优化运算时间,使增量学习的训练速度大为提高.与单纯使用支持向量代表样本数据集合进行增量学习的传统算法相比,使用该算法使分类精度得到了提高.针对肝功能检测标准数据集(BUPA)的实验验证了该算法的有效性.
A new geometric fast incremental learning algorithm for support vector machines （SVM） was proposed. A set of hull vectors most likely to become the support vectors are extracted from the training samples by using the geometric information in these samples. In the incremental learning process, the obtained hull vector set and a new sample set are conjoined as the updated training sample set, which greatly reduces the time consumed in solving sequential quadratic optimization problems in incremental SVM training and speeds up the training process. Compared with the existing incremental SVM learning algorithms in which only support vectors are used to represent the original sample set, the proposed algorithm improves the classification precision. Experiments based on a standard BUPA dataset in liver function tests validated the effectiveness of the algorithm.
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2006年第2期202-206,215,共6页
Journal of Zhejiang University：Engineering Science
基金
国家"863"高技术研究发展计划资助项目(2002AA412010)
关键词
增量算法
支持向量机
壳向量
incremental algorithm
support vector machine
hull vector
