Taiwanese Journal of Mathematics


Hongzhi Tong, Di-Rong Chen, and Fenghong Yang

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In this paper we investigate a class of learning algorithms for classification generated by regularization schemes with polynomial kernels and $l^1-$regularizer. The novelty of our analysis lies in the estimation of the hypothesis error. A Bernstein-Kantorovich polynomial is introduced as a regularizing function. Although the hypothesis spaces and the regularizers in the schemes are sample dependent, we prove the hypothesis error can be removed from the error decomposition with confidence. As a result, we derive some explicit learning rates for the produced classifiers under some assumptions.

Article information

Taiwanese J. Math., Volume 18, Number 5 (2014), 1633-1651.

First available in Project Euclid: 10 July 2017

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Zentralblatt MATH identifier

Primary: 68T05: Learning and adaptive systems [See also 68Q32, 91E40] 62J02: General nonlinear regression

classification coefficient regularization polynomial kernels Bernstein-Kantorovich polynomial learning rates


Tong, Hongzhi; Chen, Di-Rong; Yang, Fenghong. CLASSIFICATION WITH POLYNOMIAL KERNELS AND $l^1-$COEFFICIENT REGULARIZATION. Taiwanese J. Math. 18 (2014), no. 5, 1633--1651. doi:10.11650/tjm.18.2014.3929. https://projecteuclid.org/euclid.twjm/1499706530

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