Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2010

Abstract

Online learning and kernel learning are two active research topics in machine learning. Although each of them has been studied extensively, there is a limited effort in addressing the intersecting research. In this paper, we introduce a new research problem, termed Online Multiple Kernel Learning (OMKL), that aims to learn a kernel based prediction function from a pool of predefined kernels in an online learning fashion. OMKL is generally more challenging than typical online learning because both the kernel classifiers and their linear combination weights must be learned simultaneously. In this work, we consider two setups for OMKL, i.e. combining binary predictions or real-valued outputs from multiple kernel classifiers, and we propose both deterministic and stochastic approaches in the two setups for OMKL. The deterministic approach updates all kernel classifiers for every misclassified example, while the stochastic approach randomly chooses a classifier(s) for updating according to some sampling strategies. Mistake bounds are derived for all the proposed OMKL algorithms.

Keywords

On-line learning and relative loss bounds, Kernels

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Algorithmic Learning Theory: 21st International Conference, ALT 2010, Canberra, Australia, October 6-8: Proceedings

Volume

6331

First Page

390

Last Page

404

ISBN

9783642161070

Identifier

10.1007/978-3-642-16108-7_31

Publisher

Springer

City or Country

Berlin

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-642-16108-7_31

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