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Learning functional dependencies with kernel methods

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83886

Dinuzzo,  F
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Dinuzzo, F. (2010). Learning functional dependencies with kernel methods. Scientifica Acta, 4(1), MS 16-25. Retrieved from http://riviste.paviauniversitypress.it/index.php/sa/article/view/824.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C132-D
Abstract
In this paper, we review some recent research directions regarding the synthesis of functions from data using kernel methods. We start by highlighting the central role of the representer theorem and then outline some recent advances in large scale optimization, learning the kernel, and multi-task learning.