The convergence rate of semi-supervised regression with quadratic loss

Baohuai Sheng and Hancan Zhu

Applied Mathematics and Computation, 2018, vol. 321, issue C, 11-24

Abstract: It is known that the semi-supervised learning deals with learning algorithms with less labeled samples and more unlabeled samples. One of the problems in this field is to show, at what extent, the performance depends upon the unlabeled number. A kind of modified semi-supervised regularized regression with quadratic loss is provided. The convergence rate for the error estimate is given in expectation mean. It is shown that the learning rate is controlled by the number of the unlabeled samples, and the algorithm converges with the increasing of the unlabeled sample number.

Keywords: Semi-supervised regression; Quadratic loss; Ga^teaux derivative; Learning rate (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:321:y:2018:i:c:p:11-24

DOI: 10.1016/j.amc.2017.10.033

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