Improving localized multiple kernel learning via radius-margin bound
type: Item
instance of: scholarly article
title: Improving localized multiple kernel learning via radius-margin bound
zbMATH Open document ID: 1426.68242
DOI: 10.1155/2017/4579214
author: Yanyan Li
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: Localized multiple kernel learning (LMKL) is an effective method of multiple kernel learning (MKL). It tries to learn the optimal kernel from a set of predefined basic kernels by directly using the maximum margin principle, which is embodied in support vector machine (SVM). However, LMKL does not consider the radius of minimum enclosing ball (MEB) which actually impacts the error bound of SVM as well as the separating margin. In the paper, we propose an improved version of LMKL, which is named ILMKL. The proposed method explicitly takes into consideration both the margin and the radius and so achieves better performance over its counterpart. Moreover, the proposed method can automatically tune the regularization parameter when learning the optimal kernel. Consequently, it avoids using the time-consuming cross-validation process to choose the parameter. Comprehensive experiments are conducted and the results well demonstrate the effectiveness and efficiency of the proposed method.
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6972115
Wikidata QID: Q59147557
describes a project that uses: UCI-ml
describes a project that uses: SHOGUN
describes a project that uses: SimpleMKL
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/4579214
OpenAlex ID: W2570832236
cites work: Q3096204
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description: scientific article; zbMATH DE number 6972115