摘要
通过对SVDD增量学习中原样本和新增样本的特性分析,提出一种改进的SVDD增量学习算法。在增量学习过程中,该算法选取原样本的支持向量集和非支持向量中可能转为支持向量的样本集以及新增样本中违反KKT条件的样本作为训练样本集,舍弃对最终分类无用的样本。实验结果表明,该算法在保证分类精度的同时减少了训练时间。
An improved incremental learning algorithm for Support Vector Data Description（SVDD） is presented through the characteristic analysis of old samples and new samples. In the course of incremental learning, support vecter set and non-support vector set which may be converted into support vector in old samples and samples which violate Karush-Kuhn-Tucker（KKT） condition in new samples are chosen as training samples and the useless samples are discarded in this algorithm. Experimental results show that the training time is greatly reduced while the classification precision is guaranteed.
出处
《计算机工程》
CAS
CSCD
北大核心
2009年第22期210-211,215,共3页
Computer Engineering
基金
盐城工学院重点学科建设基金资助项目(XKY2007065)
关键词
支持向量数据描述
KKT条件
支持向量
增量学习
Support Vector Data Description（SVDD）
Karush-Kuhn-Tucker（KKT） condition
support vector
incremental learning
