Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4607703 | Journal of Approximation Theory | 2010 | 26 Pages |
Abstract
We propose an early stopping algorithm for learning gradients. The motivation is to choose “useful” or “relevant” variables by a ranking method according to norms of partial derivatives in some function spaces. In the algorithm, we used an early stopping technique, instead of the classical Tikhonov regularization, to avoid over-fitting.After stating dimension-dependent learning rates valid for any dimension of the input space, we present a novel error bound when the dimension is large. Our novelty is the independence of power index of the learning rates on the dimension of the input space.
Related Topics
Physical Sciences and Engineering
Mathematics
Analysis
Authors
Xin Guo,