Advances in Domain Adaptation Theory
Advances in Domain Adaptation Theory gives current, state-of-the-art results on transfer learning, with a particular focus placed on domain adaptation from a theoretical point-of-view. The book begins with a brief overview of the most popular concepts...
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Advances in Domain Adaptation Theory gives current, state-of-the-art results on transfer learning, with a particular focus placed on domain adaptation from a theoretical point-of-view. The book begins with a brief overview of the most popular concepts used to provide generalization guarantees, including sections on Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and Stability based bounds. In addition, the book explains domain adaptation problem and describes the four major families of theoretical results that exist in the literature, including the Divergence based bounds. Next, PAC-Bayesian bounds are discussed, including the original PAC-Bayesian bounds for domain adaptation and their updated version.
Additional sections present generalization guarantees based on the robustness and stability properties of the learning algorithm.
Bibliographische Angaben
- Autoren: Ievgen Redko , Emilie Morvant , Amaury Habrard
- Verlag: Elsevier Science & Technology
- EAN: 9781785482366
Autoren-Porträt von Ievgen Redko, Emilie Morvant, Amaury Habrard
Ievgen Redko is an associate professor at INSA in Lyon since 2016. He obtained his PhD in computer Science, specialized in Data Science in 2015.Emilie Morvant is a Lecturer and a professor assistant at the Jean Monnet of Saint-Etienne University. She obtained her PhD in 2013 in Computer Science.
Inhaltsverzeichnis zu „Advances in Domain Adaptation Theory “
1. Introduction2. State-of-the-art on statistical learning theory
3. Domain adaptation problem
4. Divergence based bounds
5. PAC-Bayes bounds for domain adaptation
6. Robustness and adaptation
7. Stability and hypothesis transfer learning
8. Impossibility results
9. Conclusions and open discussions
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