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Multi-Output Learning via Spectral Filtering

Author(s)
Baldassarre, Luca; Rosasco, Lorenzo; Barla, Annalisa; Verri, Alessandro
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DownloadMIT-CSAIL-TR-2011-004.pdf (786.2Kb)
Other Contributors
Center for Biological and Computational Learning (CBCL)
Advisor
Tomaso Poggio
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Abstract
In this paper we study a class of regularized kernel methods for vector-valued learning which are based on filtering the spectrum of the kernel matrix. The considered methods include Tikhonov regularization as a special case, as well as interesting alternatives such as vector-valued extensions of L2 boosting. Computational properties are discussed for various examples of kernels for vector-valued functions and the benefits of iterative techniques are illustrated. Generalizing previous results for the scalar case, we show finite sample bounds for the excess risk of the obtained estimator and, in turn, these results allow to prove consistency both for regression and multi-category classification. Finally, we present some promising results of the proposed algorithms on artificial and real data.
Date issued
2011-01-24
URI
http://hdl.handle.net/1721.1/60875
Series/Report no.
MIT-CSAIL-TR-2011-004CBCL-296
Keywords
Computational Learning, Multi-Output Learning, Spectral Methods

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