Abstract
We derive a generalization bound for domain adaptation by using the properties of robust algorithms. Our new bound depends on λ-shift, a measure of prior knowledge regarding the similarity of source and target domain distributions. Based on the generalization bound, we design SVM variants for binary classification and regression domain adaptation algorithms.
Original language | English |
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Pages (from-to) | 365-380 |
Number of pages | 16 |
Journal | Annals of Mathematics and Artificial Intelligence |
Volume | 71 |
Issue number | 4 |
DOIs | |
State | Published - 1 Aug 2014 |
Keywords
- Adaptation
- Robustness
- SVM
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Applied Mathematics