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We consider the regression learning based on the moving least-squares framework for the polynomially β-mixing samples. The rigorous error analysis is carried out by using the independent-blocks technique in the… Click to show full abstract
We consider the regression learning based on the moving least-squares framework for the polynomially β-mixing samples. The rigorous error analysis is carried out by using the independent-blocks technique in the sample error estimates. We derive the satisfactory learning rate that can be arbitrarily close to the best rate , when the sequence of marginal distributions converges exponentially to some marginal distribution in the dual of a Hölder space.
Keywords:
error analysis;
moving least;
regression learning;
least squares
Journal Title: International Journal of Computer Mathematics
Year Published: 2020
Link to full text (if available)
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Journal Title: International Journal of Computer Mathematics
Year Published: 2020
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!