In kernel methods, an interesting recent development seeks to learn a good kernel from empirical data automatically. In this paper, by regarding the transductive learning of the k...
This paper presents kernel plugins, a framework for dynamic kernel specialization inspired by ideas borrowed from virtualization research. Plugins can be created and updated inexp...
The ever increasing size and complexity of Operating System (OS) kernel code bring an inevitable increase in the number of security vulnerabilities that can be exploited by attack...
This paper investigates the use of a one-class support vector machine algorithm to detect the onset of system anomalies, and trend output classification probabilities, as a way to ...
Selecting conveniently the proposal kernel and the adjustment multiplier weights of the auxiliary particle filter may increase significantly the accuracy and computational efficie...