Publication Type

Conference Proceeding Article

Publication Date

9-2012

Abstract

Kernel-based online learning often exhibits promising empirical performance for various applications according to previous studies. However, it often suffers a main shortcoming, that is, the unbounded number of support vectors, making it unsuitable for handling large-scale datasets. In this paper, we investigate the problem of budget kernel-based online learning that aims to constrain the number of support vectors by a predefined budget when learning the kernel-based prediction function in the online learning process. Unlike the existing studies, we present a new framework of budget kernel-based online learning based on a recently proposed online learning method called “Double Updating Online Learning” (DUOL), which has shown state-of-the-art performance as compared with the other traditional kernel-based online learning algorithms. We analyze the theoretical underpinning of the proposed Budget Double Updating Online Learning (BDUOL) framework, and then propose several BDUOL algorithms by designing different budget maintenance strategies. We evaluate the empirical performance of the proposed BDUOL algorithms by comparing them with several well-known budget kernel-based online learning algorithms, in which encouraging results validate the efficacy of the proposed technique.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012: Proceedings, Part I

Volume

7523

First Page

810

Last Page

826

ISBN

9783642334597

Identifier

10.1007/978-3-642-33460-3_57

Publisher

Springer Verlag

City or Country

Berlin

Embargo Period

10-31-2015

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1007/978-3-642-33460-3_57

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