We study the problem of private classification using kernel methods. More specifically, we propose private protocols implementing the Kernel Adatron and Kernel Perceptron learning ...
We present and discuss several spatiotemporal kernels designed to mine real-life and simulated data in support of drought prediction. We implement and empirically validate these k...
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic ...
The current trend in operating systems research is to allow applications to dynamically extend the kernel to improve application performance or extend functionality, but the most ...
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...