Robust domain adaptation
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 binar...
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| Veröffentlicht in: | Annals of mathematics and artificial intelligence 2014-08, Vol.71 (4), p.365-380 |
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| container_end_page | 380 |
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| container_issue | 4 |
| container_start_page | 365 |
| container_title | Annals of mathematics and artificial intelligence |
| container_volume | 71 |
| creator | Mansour, Yishay Schain, Mariano |
| description | 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. |
| doi_str_mv | 10.1007/s10472-013-9391-5 |
| format | Article |
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| ispartof | Annals of mathematics and artificial intelligence, 2014-08, Vol.71 (4), p.365-380 |
| issn | 1012-2443 1573-7470 |
| language | eng |
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| source | SpringerNature Journals; ProQuest Central UK/Ireland; ProQuest Central |
| subjects | Adaptation Algorithms Artificial Intelligence Complex Systems Computer Science Mathematics Robustness |
| title | Robust domain adaptation |
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