深圳大学学报（理工版）
深圳大學學報（理工版）
심수대학학보（리공판）
JOURNAL OF SHENZHEN UNIVERSITY (SCIENCE & ENGINEERING)
2010年
3期
327-333
,共7页
数据挖掘%并行PSVM%入侵检测%增量学习%ε-支持向量%层叠式SVM
數據挖掘%併行PSVM%入侵檢測%增量學習%ε-支持嚮量%層疊式SVM
수거알굴%병행PSVM%입침검측%증량학습%ε-지지향량%층첩식SVM
data mining%parallel proximal support vector machine%intrusion detection%incremental learning%ε-support vector%cascade SVM
基于并行PSVM(proximal support vector machine)分类法,利用ε-支持向量与原数据集等价的特点,将PSVM和cascade SVM模型高效结合,加速训练入侵数据集.提出一种新的PSVM增量学习方法,它能快捷更新分类器.通过大量基于著名的KDD CUP 1999数据集实验,研究表明,该算法相对其他SVM方法,在保证较高检测率和较低误报率的同时,其训练时间降低80%,且能通过增量学习新数据集来有效更新分类器.
基于併行PSVM(proximal support vector machine)分類法,利用ε-支持嚮量與原數據集等價的特點,將PSVM和cascade SVM模型高效結閤,加速訓練入侵數據集.提齣一種新的PSVM增量學習方法,它能快捷更新分類器.通過大量基于著名的KDD CUP 1999數據集實驗,研究錶明,該算法相對其他SVM方法,在保證較高檢測率和較低誤報率的同時,其訓練時間降低80%,且能通過增量學習新數據集來有效更新分類器.
기우병행PSVM(proximal support vector machine)분류법,이용ε-지지향량여원수거집등개적특점,장PSVM화cascade SVM모형고효결합,가속훈련입침수거집.제출일충신적PSVM증량학습방법,타능쾌첩경신분류기.통과대량기우저명적KDD CUP 1999수거집실험,연구표명,해산법상대기타SVM방법,재보증교고검측솔화교저오보솔적동시,기훈련시간강저80%,차능통과증량학습신수거집래유효경신분류기.
A novel training method based on parallel proximal support vector machine (PSVM) classification algorithm was proposed. The efficient PSVM and the cascade SVM architecture were used to reduce the time of training through the equivalence between the ε-support vectors and the original dataset. In addition, a new incremental learning method based on PSVM was used to make the update of the classifier easier. The experiments on the KDD CUP 1999 dataset demonstrate that the training time of our methods is 20% less than that of the other SVM methods under the condition of ensuring low false positive rate and high detection rate. it can update the classifier effectively by learning the characteristics of new dataset incrementally.
