We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task da...
A central problem in learning is selection of an appropriate model. This is typically done by estimating the unknown generalization errors of a set of models to be selected from a...
Kernel supervised learning methods can be unified by utilizing the tools from regularization theory. The duality between regularization and prior leads to interpreting regularizat...
In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter ...
Abstract. Recently, an energy-based unified framework for image denoising was proposed by Mr´azek et al. [10], from which existing nonlinear filters such as M-smoothers, bilater...
Luis Pizarro, Stephan Didas, Frank Bauer, Joachim ...