Journal of Applied Mathematics

Learning Rates for l1-Regularized Kernel Classifiers

Hongzhi Tong, Di-Rong Chen, and Fenghong Yang

Full-text: Open access

Abstract

We consider a family of classification algorithms generated from a regularization kernel scheme associated with l1-regularizer and convex loss function. Our main purpose is to provide an explicit convergence rate for the excess misclassification error of the produced classifiers. The error decomposition includes approximation error, hypothesis error, and sample error. We apply some novel techniques to estimate the hypothesis error and sample error. Learning rates are eventually derived under some assumptions on the kernel, the input space, the marginal distribution, and the approximation error.

Article information

Source
J. Appl. Math., Volume 2013 (2013), Article ID 496282, 11 pages.

Dates
First available in Project Euclid: 14 March 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1394808214

Digital Object Identifier
doi:10.1155/2013/496282

Mathematical Reviews number (MathSciNet)
MR3130980

Zentralblatt MATH identifier
06950708

Citation

Tong, Hongzhi; Chen, Di-Rong; Yang, Fenghong. Learning Rates for ${l}^{1}$ -Regularized Kernel Classifiers. J. Appl. Math. 2013 (2013), Article ID 496282, 11 pages. doi:10.1155/2013/496282. https://projecteuclid.org/euclid.jam/1394808214


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