TRA:改进的SVM增量学习淘汰算法  

TRA:Improved sifting algorithm for incremental SVM learning

作　　者：张忠平[1] 薛伟[1] 王浩[1] 

机构地区：[1]燕山大学信息科学与工程学院,河北秦皇岛066004

出　　处：《计算机工程与设计》2010年第3期609-611,679,共4页Computer Engineering and Design

基　　金：国家自然科学基金项目(60773100);河北省教育厅科研计划基金项目(2006143)

摘　　要：介绍了支持向量机,报告了支持向量机增量学习算法的研究现状,分析了支持向量集在加入新样本后支持向量和非支持向量的转化情况。针对淘汰机制效率不高的问题,提出了一种改进的SVM增量学习淘汰算法——二次淘汰算法。该算法经过两次有效的淘汰,对分类无用的样本进行舍弃,使得新的增量训练在淘汰后的有效数据集进行,而无需在复杂难处理的整个训练数据集中进行,从而显著减少了后继训练时间。理论分析和实验结果表明,该算法能在保证分类精度的同时有效地提高训练速度。The support vector machine,is reported current research of incremental SVM learning algorithm.The transformation between support vectors and normal vectors during new samples added to support vector set is analyzed.Aimed at the inefficient removing method, an improved sifting algorithm for incremental SVM learning——twice removing algorithm is proposed.In this algorithm,the useless samples are discarded by two useful removing methods,leads to new incremental training choose removing effective dataset instead of using the whole dataset they can not deal easily with very large dadasets,it can reduce subsequence training time.The theoretical analysis and experimental results show that this algorithm can not only improve the training speed,but also guarantee the classification precision.

关 键 词：分类 支持向量机 KKT条件 增量学习 淘汰机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]