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.
|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