|
Transfer Learning With Adaptive Regularizers
Ulrich Rückert and Marius Kloft
In:
Proceedings of the European Conference on Machine Learning
(2011)
Springer
, Athens, Greece
.
Abstract The success of regularized risk minimization approaches to
classication 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 di-
cult to nd 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 parame-
ter values for a `2-style regularizer with feature weights. We analytically
investigate a moment-based method to obtain good values and give uni-
form 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 classicatio [Edit]
|
