CAJ | 학술논문

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.
통과대SVDD증량학습중원양본화신증양본적특성분석,제출일충개진적SVDD증량학습산법.재증량학습과정중,해산법선취원양본적지지향량집화비지지향량중가능전위지지향량적양본집이급신증양본중위반KKT조건적양본작위훈련양본집,사기대최종분류무용적양본.실험결과표명,해산법재보증분류정도적동시감소료훈련시간.
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.