NDLI: Bayesian online multi-task learning using regularization networks

Content Provider	IEEE Xplore Digital Library
Author	Pillonetto, G. Dinuzzo, F. De Nicolao, G.
Copyright Year	2008
Description	Author affiliation: Dipt. di Ing. dell'Inf., Padova Univ., Padova (Pillonetto, G.)
Abstract	Recently, standard single-task kernel methods have been extended to the case of multi-task learning under the framework of regularization. Experimental results have shown that such an approach can perform much better than single-task techniques, especially when few examples per task are available. However, a possible drawback may be computational complexity. For instance, when using regularization networks, complexity scales as the cube of the overall number of data associated with all the tasks. In this paper, an efficient computational scheme is derived for a widely applied class of multi-task kernels. More precisely, a quadratic loss is assumed and the multi-task kernel is the sum of a common term and a task-specific one. The proposed algorithm performs online learning recursively updating the estimates as new data become available. The learning problem is formulated in a Bayesian setting. The optimal estimates are obtained by solving a sequence of subproblems which involve projection of random variables onto suitable subspaces. The algorithm is tested on a simulated data set.
Starting Page	4517
Ending Page	4522
File Size	249900
Page Count	6
File Format	PDF
ISBN	9781424420780
ISSN	07431619
DOI	10.1109/ACC.2008.4587207
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Bayesian methods Kernel Machine learning Recursive estimation Computational complexity Random variables Machine learning algorithms Testing Kalman filters Filtering Kalman filtering multi-task learning machine learning kernel methods regularization Bayesian estimation
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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