- We will send in 10–14 business days.
/images/books/12214891/9781785482366.jpg)
- Author: Ievgen Redko
- Publisher: ISTE Press - Elsevier
- Year: 2017
- ISBN-10: 178548236X
- ISBN-13: 9781785482366
- Format: 15.2 x 22.9 x 1.3 cm, kieti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Reviews
Description
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.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 23d.17:58:31
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Ievgen Redko
- Publisher: ISTE Press - Elsevier
- Year: 2017
- ISBN-10: 178548236X
- ISBN-13: 9781785482366
- Format: 15.2 x 22.9 x 1.3 cm, kieti viršeliai
- Language: English English
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.
Reviews