Kernels for multi-task learning.
Advances in Neural Information Processing Systems.
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 . 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.
|Title:||Kernels for multi-task learning|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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