Comments on real-valued negative selection vs. real-valued positive selection and one-class SVM

被引：7

作者：

Stibor, Thomas ^{[1
]}

Timmis, Jonathan ^{[2
]}

机构：

[1] Tech Univ Darmstadt, Dept Comp Sci, D-64289 Darmstadt, Germany

[2] Univ York, Dept Elect, Dept Comp Sci, York YO10 5DD, N Yorkshire, England

来源：

2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS | 2007年

关键词：

D O I：

10.1109/CEC.2007.4424956

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Real-valued negative selection (RVNS) is an immune-inspired technique for anomaly detection problems. It has been claimed that this technique is a competitive approach, comparable to statistical anomaly detection approaches such as one-class Support Vector Machine. Moreover, it has been claimed that the complementary approach to RVNS, termed real-valued positive selection, is not a realistic solution. We investigate these claims and show that these claims can not be sufficiently supported.

引用

页码：3727 / +

页数：3

共 27 条

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