Given sets of observations of training and test data, we consider the problem of re-weighting the training data such that its distribution more closely matches that of the test data. We achieve thisâ€¦ (More)

In this paper, we consider collaborative filtering as a ranking problem. We present a method which uses Maximum Margin Matrix Factorization and optimizes ranking instead of rating. We employâ€¦ (More)

Despite their successes, what makes kernel methods difficult to use in many large scale problems is the fact that computing the decision function is typically expensive, especially at predictionâ€¦ (More)

kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of Râ€™s new S4 object model and provides a framework for creating and using kernelbased algorithms.â€¦ (More)

A new algorithm for Support Vector regression is described. For a pri ori chosen , it automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction of the dataâ€¦ (More)

In this thesis we consider statistical learning problems and machines. A statistical learning machine tries to infer rules from a given set of examples such that it is able to make correctâ€¦ (More)

Algorithms based on Mercer kernels construct their solutions in terms of expansions in a high-dimensional feature space F. Previous work has shown that all algorithms which can be formulated in termsâ€¦ (More)

We provide a new linear program to deal with classification of data in the case of functions written in terms of pairwise proximities. This allows to avoid the problems inherent in using featureâ€¦ (More)

Suppose you are given some dataset drawn from an underlying p robability distributionP and you want to estimate a subset S of input space such that the probability that a test point drawn from P liesâ€¦ (More)

We have recently proposed a new approach to control the number of basis functions and the accuracy in Support Vector Machines. The latter is transferred to a linear programming setting, whichâ€¦ (More)