计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
10期
100-104
,共5页
网络入侵检测%增量式支持向量机%备用集%改进的核函数
網絡入侵檢測%增量式支持嚮量機%備用集%改進的覈函數
망락입침검측%증량식지지향량궤%비용집%개진적핵함수
network intrusion detection%incremental support vector machine%reserved set%modified kernel function
针对传统的增量式支持向量机(Incremental Support Vector Machine,ISVM)在处理数据集时易受数据噪声和学习过程中振荡问题影响的缺点,将改进的核函数U-RBF和构造备用集的同心圆方法相结合,提出了基于备用集的增量式支持向量机(Reserved Set-Incremental Support Vector Machine,RS-ISVM)方法.该方法首先将特征属性的均值和均方差值嵌入到核函数RBF中,并通过同心圆方法将后续学习过程中最有可能成为支持向量的样本划入备用集.入侵检测实验证明RS-ISVM能够降低学习过程的振荡现象,提高了学习的速度,有非常好的性能和可靠性.
針對傳統的增量式支持嚮量機(Incremental Support Vector Machine,ISVM)在處理數據集時易受數據譟聲和學習過程中振盪問題影響的缺點,將改進的覈函數U-RBF和構造備用集的同心圓方法相結閤,提齣瞭基于備用集的增量式支持嚮量機(Reserved Set-Incremental Support Vector Machine,RS-ISVM)方法.該方法首先將特徵屬性的均值和均方差值嵌入到覈函數RBF中,併通過同心圓方法將後續學習過程中最有可能成為支持嚮量的樣本劃入備用集.入侵檢測實驗證明RS-ISVM能夠降低學習過程的振盪現象,提高瞭學習的速度,有非常好的性能和可靠性.
침대전통적증량식지지향량궤(Incremental Support Vector Machine,ISVM)재처리수거집시역수수거조성화학습과정중진탕문제영향적결점,장개진적핵함수U-RBF화구조비용집적동심원방법상결합,제출료기우비용집적증량식지지향량궤(Reserved Set-Incremental Support Vector Machine,RS-ISVM)방법.해방법수선장특정속성적균치화균방차치감입도핵함수RBF중,병통과동심원방법장후속학습과정중최유가능성위지지향량적양본화입비용집.입침검측실험증명RS-ISVM능구강저학습과정적진탕현상,제고료학습적속도,유비상호적성능화가고성.
Due to the shortcomings of data noise and the learning process’s oscillation problem when traditional incremental support vector machine deals with data samples, an incremental SVM based on reserved set is proposed, which combines modified kernel function with concentric circle method to structure the reserved set. The proposed method embeds the mean and mean square difference values of feature attributes in kernel function RBF. In order to shorten the training time, a concentric circle method is suggested to be used in selecting samples to form the reserved set. The intrusion detection experiments show that RS-ISVM can ease the oscillation phenomenon in the learning process and achieve pretty good performance, meanwhile, its reliability is relative high.
