Advances in Domain Adaptation Theory
| By: | Ievgen Redko; Emilie Morvant; Amaury Habrard; Marc Sebban; Younès Bennani |
| Publisher: | Elsevier S & T |
| Print ISBN: | 9781785482366 |
| eText ISBN: | 9780081023471 |
| Edition: | 0 |
| Copyright: | 2020 |
| Format: | Reflowable |
eBook Features
Instant Access
Purchase and read your book immediately
Read Offline
Access your eTextbook anytime and anywhere
Study Tools
Built-in study tools like highlights and more
Read Aloud
Listen and follow along as Bookshelf reads to you
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.
- Gives an overview of current results on transfer learning
- Focuses on the adaptation of the field from a theoretical point-of-view
- Describes four major families of theoretical results in the literature
- Summarizes existing results on adaptation in the field
- Provides tips for future research