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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 4/2012

01-12-2012 | Original Article

Learning rates of least-square regularized regression with strongly mixing observation

Authors: Yongquan Zhang, Feilong Cao, Canwei Yan

Published in: International Journal of Machine Learning and Cybernetics | Issue 4/2012

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Abstract

This paper considers the regularized learning algorithm associated with the least-square loss, strongly mixing observations and reproducing kernel Hilbert spaces. We first give the bound of the sample error with exponentially strongly mixing observations and the rate of approximation by Jackson-type theorem of approximation based on exponentially strongly mixing sequence. Then the generalization error of the least-square regularized regression is obtained by estimating sample error and regularization error.
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Metadata
Title
Learning rates of least-square regularized regression with strongly mixing observation
Authors
Yongquan Zhang
Feilong Cao
Canwei Yan
Publication date
01-12-2012
Publisher
Springer-Verlag
Published in
International Journal of Machine Learning and Cybernetics / Issue 4/2012
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-011-0058-4

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