Similarity matrices generated from many applications may not be positive semidefinite, and hence can't fit into the kernel machine framework. In this paper, we study the prob...
When given a small sample, we show that classification with SVM can be considerably enhanced by using a kernel function learned from the training data prior to discrimination. Thi...
Abstract. In order to detect a compromise of a running process based on it deviating from its program’s normal system-call behavior, an anomaly detector must first be trained wi...
— 1 In this paper, training-based transmissions over a priori unknown Rayleigh block fading channels are considered. The input signals are assumed to be subject to peak power con...
Abstract. In this paper we discuss sparse least squares support vector regressors (sparse LS SVRs) defined in the reduced empirical feature space, which is a subspace of mapped tr...