Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2010

Abstract

In this paper, we investigate a new machine learning framework called Online Transfer Learning (OTL) that aims to transfer knowledge from some source domain to an online learning task on a target domain. We do not assume the target data follows the same class or generative distribution as the source data, and our key motivation is to improve a supervised online learning task in a target domain by exploiting the knowledge that had been learned from large amount of training data in source domains. OTL is in general challenging since data in both domains not only can be different in their class distributions but can be also different in their feature representations. As a first attempt to this problem, we propose techniques to address two kinds of OTL tasks: one is to perform OTL in a homogeneous domain, and the other is to perform OTL across heterogeneous domains. We show the mistake bounds of the proposed OTL algorithms, and empirically examine their performance on several challenging OTL tasks. Encouraging results validate the efficacy of our techniques.

Keywords

Transfer learning, Online learning, Knowledge transfer

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 27th International Conference on Machine Learning (ICML 2010): Haifa, Israel, 21-24 June

First Page

219-1

Last Page

9

ISBN

9781632660596

Publisher

International Machine Learning Society

City or Country

Madison, WI

Copyright Owner and License

Authors

Additional URL

https://icml.cc/Conferences/2010/papers/219.pdf

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