Abstract
We present the first result for kernel regression where the procedure adapts locally at a point x to both the unknown local dimension of the metric space χ and the unknown Hölder-continuity of the regression function at x. The result holds with high probability simultaneously at all points x in a general metric space χ of unknown structure.
| Original language | English |
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| Journal | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS |
| Publication status | Published - 2013 |
| MoE publication type | A4 Article in a conference publication |
| Event | IEEE Conference on Neural Information Processing Systems - Lake Tahoe, United States Duration: 5 Dec 2013 → 10 Dec 2013 |