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Section A

Coefficient regularized regression with non-iid sampling

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Pages 3113-3124 | Received 19 Aug 2010, Accepted 09 May 2011, Published online: 01 Aug 2011
 

Abstract

In this paper, we study a more general kernel regression learning with coefficient regularization. A non-iid setting is considered, where the sequence of probability measures for sampling is not identical but the sequence of marginal distributions for sampling converges exponentially fast in the dual of a Holder space; the sampling z i , i ≥ 1 are weakly dependent, which satisfy a strongly mixing condition. Satisfactory capacity independently error bounds and learning rates are derived by the techniques of integral operator for this learning algorithm.

2000 AMS Subject Classifications :

Acknowledgements

The work described in this paper is supported by the Natural Science Foundation of China (Grant no. 11071276) and the Nature Science Fund of Shandong Province, China (Grant no. Y2007A11).

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