An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context
January 13, 2015 ยท Declared Dead ยท ๐ Neural Information Processing Systems
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Authors
Pascal Germain, Amaury Habrard, Francois Laviolette, Emilie Morvant
arXiv ID
1501.03002
Category
stat.ML: Machine Learning (Stat)
Cross-listed
cs.LG
Citations
0
Venue
Neural Information Processing Systems
Last Checked
4 months ago
Abstract
This paper provides a theoretical analysis of domain adaptation based on the PAC-Bayesian theory. We propose an improvement of the previous domain adaptation bound obtained by Germain et al. in two ways. We first give another generalization bound tighter and easier to interpret. Moreover, we provide a new analysis of the constant term appearing in the bound that can be of high interest for developing new algorithmic solutions.
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