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Online learning with (multiple) kernels : a review
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Diethe, Tom and Girolami, Mark (2013) Online learning with (multiple) kernels : a review. Neural Computation, Volume 25 (Number 3). pp. 567-625. doi:10.1162/NECO_a_00406
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Official URL: http://dx.doi.org/10.1162/NECO_a_00406
Abstract
This review examines kernel methods for online learning, in particular, multiclass classification. We examine margin-based approaches, stemming from Rosenblatt's original perceptron algorithm, as well as nonparametric probabilistic approaches that are based on the popular gaussian process framework. We also examine approaches to online learning that use combinations of kernels--online multiple kernel learning. We present empirical validation of a wide range of methods on a protein fold recognition data set, where different biological feature types are available, and two object recognition data sets, Caltech101 and Caltech256, where multiple feature spaces are available in terms of different image feature extraction methods.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics | ||||||
| Divisions: | Faculty of Science > Statistics | ||||||
| Library of Congress Subject Headings (LCSH): | Kernel functions, Machine learning | ||||||
| Journal or Publication Title: | Neural Computation | ||||||
| Publisher: | MIT Press Journals | ||||||
| ISSN: | 0899-7667 | ||||||
| Official Date: | March 2013 | ||||||
| Dates: |
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| Date of first compliant deposit: | 28 December 2015 | ||||||
| Volume: | Volume 25 | ||||||
| Number: | Number 3 | ||||||
| Page Range: | pp. 567-625 | ||||||
| DOI: | 10.1162/NECO_a_00406 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access | ||||||
| Funder: | NCR Financial Solutions Group |
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