Transfer Learning with Adaptive Regularizers
Abstract
The success of regularized risk minimization approaches to classification with linear models depends crucially on the selection of a regularization term that matches with the learning task at hand. If the necessary domain expertise is rare or hard to formalize, it may be difficult to find a good regularizer. On the other hand, if plenty of related or similar data is available, it is a natural approach to adjust the regularizer for the new learning problem based on the characteristics of the related data. In this paper, we study the problem of obtaining good parameter values for a ℓ_2-style regularizer with feature weights. We analytically investigate a moment-based method to obtain good values and give uniform convergence bounds for the prediction error on the target learning task. An empirical study shows that the approach can improve predictive accuracy considerably in the application domain of text classification.
Cite
Text
Rückert and Kloft. "Transfer Learning with Adaptive Regularizers." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011. doi:10.1007/978-3-642-23808-6_5Markdown
[Rückert and Kloft. "Transfer Learning with Adaptive Regularizers." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011.](https://mlanthology.org/ecmlpkdd/2011/ruckert2011ecmlpkdd-transfer/) doi:10.1007/978-3-642-23808-6_5BibTeX
@inproceedings{ruckert2011ecmlpkdd-transfer,
title = {{Transfer Learning with Adaptive Regularizers}},
author = {Rückert, Ulrich and Kloft, Marius},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2011},
pages = {65-80},
doi = {10.1007/978-3-642-23808-6_5},
url = {https://mlanthology.org/ecmlpkdd/2011/ruckert2011ecmlpkdd-transfer/}
}