Metric-Based Methods for Adaptive Model Selection and Regularization
Full Text: schuurmans.pdf
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea is to impose a metric structure on hypotheses by determining the discrepancy between their predictions across the distribution of unlabeled data.We showhowthis metric can be used to detect untrustworthy training error estimates, and devise novel model selection strategies that exhibit theoretical guarantees against over-fitting (while still avoiding under-fitting).We then extend the approach to derive a general training criterion for supervised learningÂyielding an adaptive regularization method that uses unlabeled data to automatically set regularization parameters. This new criterion adjusts its regularization level to the specific set of training data received, and performs well on a variety of regression and conditional density estimation tasks. The only proviso for these methods is that sufficient unlabeled training data be available.
Citation
D. Schuurmans, F. Southey. "Metric-Based Methods for Adaptive Model Selection and Regularization". Machine Learning Journal (MLJ), 48(1-3), pp 51-84, January 2002.| Keywords: | model selection, regularization, unlabeled examples, machine learning |
| Category: | In Journal |
BibTeX
@article{Schuurmans+Southey:MLJ02,
author = {Dale Schuurmans and Finnegan Southey},
title = {Metric-Based Methods for Adaptive Model Selection and
Regularization},
Volume = "48",
Number = "1-3",
Pages = {51-84},
journal = {Machine Learning Journal (MLJ)},
year = 2002,
}Last Updated: September 20, 2009Submitted by Dale Schuurmans
