UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Kernels for multi-task learning

Micchelli, CA; Pontil, M; (2005) Kernels for multi-task learning. In:

Full text not available from this repository.


This paper provides a foundation for multi-task learning using reproducing kernel Hilbert spaces of vector-valued functions. In this setting, the kernel is a matrix-valued function. Some explicit examples will be described which go beyond our earlier results in [7]. In particular, we characterize classes of matrix- valued kernels which are linear and are of the dot product or the translation invariant type. We discuss how these kernels can be used to model relations between the tasks and present linear multi-task learning algorithms. Finally, we present a novel proof of the representer theorem for a minimizer of a regularization functional which is based on the notion of minimal norm interpolation.

Type: Proceedings paper
Title: Kernels for multi-task learning
ISBN: 0262195348
UCL classification: UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/158516
Downloads since deposit
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item