文献类型：期刊文献

英文题名：The convergence rate of semi-supervised regression with quadratic loss

作者：Sheng, Baohuai[1];Zhu, Hancan[1]

机构：[1]Shaoxing Univ, Dept Math, Shaoxing 312000, Peoples R China

年份：2018

卷号：321

起止页码：11

外文期刊名：APPLIED MATHEMATICS AND COMPUTATION

收录：SCI-EXPANDED(收录号：WOS:000417521600002)、、EI(收录号：20174604387241)、Scopus(收录号：2-s2.0-85033223196)、WOS

基金：This work is supported by the National Natural Science Foundation of China under Grants (Nos. 11471292, 61602307).

语种：英文

外文关键词：Semi-supervised regression; Quadratic loss; Gateaux derivative; Learning rate

外文摘要：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. (C) 2017 Elsevier Inc. All rights reserved.