| 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.
Related Topics
Physical Sciences and Engineering
Mathematics
Analysis
