We study the problem of learning using combinations of machines. In particular we present new theoretical bounds on the generalization performance of voting ensembles of kernel ma...
In this paper we use doubly stochastic matrices to establish a link between Birkhoff polytopes and heat kernels on graphs. Based on this analysis we construct a multi-dimensional ...
Francisco Escolano, Edwin R. Hancock, Miguel Angel...
Abstract. The convex optimisation problem involved in fitting a kernel probit regression (KPR) model can be solved efficiently via an iteratively re-weighted least-squares (IRWLS)...
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...
The automatic extraction of relations between entities expressed in natural language text is an important problem for IR and text understanding. In this paper we show how differen...