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 ISSN 0899-7667.

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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, Engineering and Medicine > 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:	Date Event March 2013 Published 5 February 2013 Available
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 (Creative Commons)
Date of first compliant deposit:	28 December 2015
Date of first compliant Open Access:	28 December 2015
Funder:	NCR Financial Solutions Group

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