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.
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This research was supported in by The Israeli Centers of Research Excellence (I-CORE) program, (Center No. 4/11), by a grant from the Israel Science Foundation, by a grant from United States-Israel Binational Science Foundation (BSF), and by a grant from the Israeli Ministry of Science (MoS).
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Mansour, Y., Schain, M. Robust domain adaptation. Ann Math Artif Intell 71, 365–380 (2014). https://doi.org/10.1007/s10472-013-9391-5
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DOI: https://doi.org/10.1007/s10472-013-9391-5