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The convergence rate of semi-supervised regression with quadratic loss

Author

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  • Sheng, Baohuai
  • Zhu, Hancan

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.

Suggested Citation

  • Sheng, Baohuai & Zhu, Hancan, 2018. "The convergence rate of semi-supervised regression with quadratic loss," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 11-24.
    • Handle: RePEc:eee:apmaco:v:321:y:2018:i:c:p:11-24
    DOI: 10.1016/j.amc.2017.10.033
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    References listed on IDEAS

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    1. Michael Kohler, 2002. "Universal Consistency of Local Polynomial Kernel Regression Estimates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(4), pages 879-899, December.
    2. Harro Walk, 2005. "Strong universal consistency of smooth kernel regression estimates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(4), pages 665-685, December.
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