Kernel Principal Component Analysis extends linear PCA from a Euclidean space to any reproducing kernel Hilbert space. Robustness issues for Kernel PCA are studied. The sensitivit...
Kernel methods offer a flexible toolbox for pattern analysis and machine learning. A general class of kernel functions which incorporates known pattern invariances are invariant d...
This paper describes an experimental study of Linux kernel behavior in the presence of errors that impact the instruction stream of the kernel code. Extensive error injection exper...
Weining Gu, Zbigniew Kalbarczyk, Ravishankar K. Iy...
In this paper we are concerned with reproducing kernel Hilbert spaces HK of functions from an input space into a Hilbert space Y, an environment appropriate for multi-task learnin...
Andrea Caponnetto, Charles A. Micchelli, Massimili...