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1 Covariate Shift by Kernel Mean Matching

• Arthur Gretton, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt, Bernhard Schölkopf
• 2008

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 this… (More)

Maximum Margin Matrix Factorization for Collaborative Ranking

• Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alex Smola
• 2007

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 employ… (More)

Fastfood – Approximating Kernel Expansions in Loglinear Time

• Tamás Sarlós, Alex Smola
• 2013

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 prediction… (More)

kernlab – An S 4 Package for Kernel Methods in R

• Alexandros Karatzoglou, Alex Smola, Kurt Hornik
• 2004

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

Shrinking the Tube : A New Support Vector Regression Algorithm

• Bernhard Schölkopf, Peter Bartlett, Alex Smola, Robert C. Williamson
• 1999

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 data… (More)

Kernel Fisher Discriminants

• Dipl Inform Sebastian, Mika, +32 authors Sebastian Mika
• 2003

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 correct… (More)

Kernel Pca Pattern Reconstruction via Approximate Pre-images 1 Kernels and Feature Spaces

• Sebastian Mika, Alex Smola, Gunnar R atsch, M Klaus-Robert, ullerGMD, Rudower Chaussée
• 1998

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 terms… (More)

Classification on Proximity Data with LP – Machines

• Thore Graepel, Ralf Herbrich, Bernhard Schölkopf, Alex Smola, Peter Bartlett, Klaus Obermayer
• 1999

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 feature… (More)

Accepted for NIPS ’ 99 SV Estimation of a Distribution ’ s Support

• Bernhard Schölkopf, Robert C. Williamson, Alex Smola, John Shawe-Taylor
• 1999

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 lies… (More)

Linear Programs for Automatic Accuracy Control in Regression

• Alex Smola, Bernhard Schölkopf, Gunnar Rätsch, Rudower Chaussée
• 1999

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, which… (More)

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