Sciweavers

ICML
2008
IEEE

Composite kernel learning

14 years 4 months ago
Composite kernel learning
The Support Vector Machine (SVM) is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Multiple Kernel Learning (MKL) enables to learn the kernel, from an ensemble of basis kernels, whose combination is optimized in the learning process. Here, we propose Composite Kernel Learning to address the situation where distinct components give rise to a group structure among kernels. Our formulation of the learning problem encompasses several setups, putting more or less emphasis on the group structure. We characterize the convexity of the learning problem, and provide a general wrapper algorithm for computing solutions. Finally, we illustrate the behavior of our method on multi-channel data where groups correpond to channels.
Marie Szafranski, Yves Grandvalet, Alain Rakotomam
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2008
Where ICML
Authors Marie Szafranski, Yves Grandvalet, Alain Rakotomamonjy
Comments (0)