Contents Online
Communications in Mathematical Sciences
Volume 16 (2018)
Number 5
Regularized semi-supervised least squares regression with dependent samples
Pages: 1347 – 1360
DOI: https://dx.doi.org/10.4310/CMS.2018.v16.n5.a8
Authors
Abstract
In this paper, we study regularized semi-supervised least squares regression with dependent samples. We analyze the regularized algorithm based on reproducing kernel Hilbert spaces, and show, with the use of unlabelled data that the regularized least squares algorithm can achieve the nearly minimax optimal learning rate with a logarithmic term for dependent samples. Our new results are better than existing results in the literature.
Keywords
semi-supervised learning, regularization, least squares regression, non-iid sampling
2010 Mathematics Subject Classification
62J02, 68T05
Received 25 December 2017
Received revised 28 April 2018
Accepted 28 April 2018
Published 19 December 2018