An adaptive regularization criterion for supervised learning
Full Text: schuurmans00adaptive.pdf
We introduce a new regularization criterion that exploits unlabeled data to adaptively control hypothesis-complexity in general supervised learning tasks. The technique is based on an abstract metric-space view of supervised learning that has been successfully applied to model selection in previous research. The new regularization criterion we introduce involves no free parameters and yet performs well on a variety of regression and conditional density estimation tasks. The only...
Citation
D. Schuurmans, F. Southey. "An adaptive regularization criterion for supervised learning". International Conference on Machine Learning (ICML), Stanford University, June 2000.| Keywords: | |
| Category: | In Conference |
BibTeX
@incollection{Schuurmans+Southey:ICML00,
author = {Dale Schuurmans and Finnegan Southey},
title = {An adaptive regularization criterion for supervised learning},
booktitle = {International Conference on Machine Learning (ICML)},
year = 2000,
}Last Updated: June 01, 2007Submitted by Staurt H. Johnson
