The nature of statistical learning theory
The nature of statistical learning theory
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
The covering number in learning theory
Journal of Complexity
Learning Theory: An Approximation Theory Viewpoint (Cambridge Monographs on Applied & Computational Mathematics)
Multi-kernel regularized classifiers
Journal of Complexity
Support Vector Machines
Moving least-square method in learning theory
Journal of Approximation Theory
Capacity of reproducing kernel spaces in learning theory
IEEE Transactions on Information Theory
Statistical analysis of the moving least-squares method with unbounded sampling
Information Sciences: an International Journal
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In this paper we apply a concentration technique to improve the convergence rates for a moving least-square learning algorithm for regression. The concentration technique allows us to obtain a sharper bound for the sample error.