Adaptation for Regularization Operators in Learning Theory
We consider learning algorithms induced by regularization methods in the regression setting. We show that previously obtained error bounds for these algorithms using a-priori choices of the regularization parameter, can be attained using a suitable a-posteriori choice based on validation. In parti...
| Päätekijät: | , |
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| Kieli: | en_US |
| Julkaistu: |
2006
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| Aiheet: | |
| Linkit: | http://hdl.handle.net/1721.1/34217 |