Open Access
2013 Least Square Regularized Regression for Multitask Learning
Yong-Li Xu, Di-Rong Chen, Han-Xiong Li
Abstr. Appl. Anal. 2013(SI32): 1-7 (2013). DOI: 10.1155/2013/715275


The study of multitask learning algorithms is one of very important issues. This paper proposes a least-square regularized regression algorithm for multi-task learning with hypothesis space being the union of a sequence of Hilbert spaces. The algorithm consists of two steps of selecting the optimal Hilbert space and searching for the optimal function. We assume that the distributions of different tasks are related to a set of transformations under which any Hilbert space in the hypothesis space is norm invariant. We prove that under the above assumption the optimal prediction function of every task is in the same Hilbert space. Based on this result, a pivotal error decomposition is founded, which can use samples of related tasks to bound excess error of the target task. We obtain an upper bound for the sample error of related tasks, and based on this bound, potential faster learning rates are obtained compared to single-task learning algorithms.


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Yong-Li Xu. Di-Rong Chen. Han-Xiong Li. "Least Square Regularized Regression for Multitask Learning." Abstr. Appl. Anal. 2013 (SI32) 1 - 7, 2013.


Published: 2013
First available in Project Euclid: 26 February 2014

zbMATH: 07095268
MathSciNet: MR3147794
Digital Object Identifier: 10.1155/2013/715275

Rights: Copyright © 2013 Hindawi

Vol.2013 • No. SI32 • 2013
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