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 |
|---|---|
| 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
ASJC Scopus subject areas
- Applied Mathematics
- Artificial Intelligence
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