Stability and Hypothesis Transfer Learning
| Type of publication: | Conference paper |
| Citation: | Kuzborskij_ICML_2013 |
| Publication status: | Accepted |
| Booktitle: | International Conference on Machine Learning |
| Year: | 2013 |
| Month: | June |
| Abstract: | We consider the transfer learning scenario, where the learner does not have access to the source domain directly, but rather operates on the basis of hypotheses induced from it - the Hypothesis Transfer Learning (HTL) problem. Particularly, we conduct a theoretical analysis of HTL by considering the algorithmic stability of a class of HTL algorithms based on Regularized Least Squares with biased regularization. We show that the relatedness of source and target domains accelerates the convergence of the Leave-One-Out error to the generalization error, thus enabling the use of the Leave-One-Out error to find the optimal transfer parameters, even in the presence of a small training set. In case of unrelated domains we also suggest a theoretically principled way to prevent negative transfer, so that in the limit we recover the performance of the algorithm not using any knowledge from the source domain. |
| Keywords: | domain adaptation, learning theory, leave-one-out, regularized least squares, stability, transfer learning |
| Projects: |
Idiap NINAPRO |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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