Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2011

Abstract

Multiple kernel learning (MKL) has been shown as a promising machine learning technique for data mining tasks by integrating with multiple diverse kernel functions. Traditional MKL methods often formulate the problem as an optimization task of learning both optimal combination of kernels and classifiers, and attempt to resolve the challenging optimization task by various techniques. Unlike the existing MKL methods, in this paper, we investigate a boosting framework of exploring multiple kernel learning for classification tasks. In particular, we present a novel framework of Multiple Kernel Boosting (MKBoost), which applies boosting techniques for learning kernel-based classifiers with multiple kernels. Based on the proposed framework, we develop several variants of MKBoost algorithms and examine their empirical performance in comparisons to several state-of-the-art MKL algorithms on classification tasks. Experimental results show that the proposed method is more effective and efficient than the existing MKL techniques.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2011 SIAM International Conference on Data Mining: April 28-30, Mesa, AZ

First Page

199

Last Page

210

ISBN

9780898719925

Identifier

10.1137/1.9781611972818.18

Publisher

SIAM

City or Country

Philadelphia, PA

Additional URL

https://doi.org/10.1137/1.9781611972818.18

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