112
Views
4
CrossRef citations to date
0
Altmetric
Original Article

Error analysis of the moving least-squares method with non-identical sampling

ORCID Icon &
Pages 767-781 | Received 11 Jan 2018, Accepted 19 Apr 2018, Published online: 07 May 2018
 

ABSTRACT

We derive the convergence rate of the moving least-squares learning algorithm for regression under the assumption that the samples are drawn from a non-identical sequence of probability measures. The error analysis is carried out by analysing the drift error and using the probability inequalities for the non-identical sampling. When the sequence of marginal distributions converges exponentially to marginal distribution in the dual of a Hölder space, we obtain the satisfactory capacity dependent error bounds of the algorithm that can be arbitrarily close to the rate O(m1).

2010 AMS subject Classifications:

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work is supported by the National Natural Science Foundation of China [Grant Nos. 11271199, 11671213].

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.