Least Square Regularized Regression for Multitask Learning
Joint Authors
Li, Han-Xiong
Xu, Yong-Li
Chen, Di-Rong
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-21
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Xu, Yong-Li& Chen, Di-Rong& Li, Han-Xiong. 2013. Least Square Regularized Regression for Multitask Learning. Abstract and Applied Analysis،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-492788
Modern Language Association (MLA)
Xu, Yong-Li…[et al.]. Least Square Regularized Regression for Multitask Learning. Abstract and Applied Analysis No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-492788
American Medical Association (AMA)
Xu, Yong-Li& Chen, Di-Rong& Li, Han-Xiong. Least Square Regularized Regression for Multitask Learning. Abstract and Applied Analysis. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-492788
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-492788