Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4605443 | Applied and Computational Harmonic Analysis | 2009 | 6 Pages |
Abstract
By the aid of the properties of the square root of positive operators we refine the consistency analysis of regularized least square regression in a reproducing kernel Hilbert space. Sharper error bounds and faster learning rates are obtained when the sampling sequence satisfies a strongly mixing condition.
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