Publication Type

Journal Article

Version

acceptedVersion

Publication Date

2-2013

Abstract

Although both online learning and kernel learning have been studied extensively in machine learning, there is limited effort in addressing the intersecting research problems of these two important topics. As an attempt to fill the gap, we address a new research problem, termed Online Multiple Kernel Classification (OMKC), which learns a kernel-based prediction function by selecting a subset of predefined kernel functions in an online learning fashion. OMKC is in general more challenging than typical online learning because both the kernel classifiers and the subset of selected kernels are unknown, and more importantly the solutions to the kernel classifiers and their combination weights are correlated. The proposed algorithms are based on the fusion of two online learning algorithms, i.e., the Perceptron algorithm that learns a classifier for a given kernel, and the Hedge algorithm that combines classifiers by linear weights. We develop stochastic selection strategies that randomly select a subset of kernels for combination and model updating, thus improving the learning efficiency. Our empirical study with 15 data sets shows promising performance of the proposed algorithms for OMKC in both learning efficiency and prediction accuracy

Keywords

Online learning, Kernel methods, Multiple kernels, Perceptron, Hedge, Classification

Discipline

Computer Sciences | Databases and Information Systems

Publication

Machine Learning

Volume

90

Issue

2

First Page

289

Last Page

316

ISSN

0885-6125

Identifier

10.1007/s10994-012-5319-2

Publisher

Springer

Copyright Owner and License

Authors

Additional URL

http://doi.org/10.1007/s10994-012-5319-2

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