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Advances in Domain Adaptation Theory

By: Ievgen Redko,Emilie Morvant,Amaury Habrard,Marc Sebban,Younès Bennani

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Advances in Domain Adaptation Theory

By: Ievgen Redko,Emilie Morvant,Amaury Habrard,Marc Sebban,Younès Bennani

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

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Page Count

210

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Elsevier

Published Date

ISBN 10

0081023472

ISBN 13

9780081023471

Advances in Domain Adaptation Theory

By: Ievgen Redko,Emilie Morvant,Amaury Habrard,Marc Sebban,Younès Bennani

Write a review Read reviews Take a Quiz Solve Book Puzzle

Advances in Domain Adaptation Theory

By: Ievgen Redko,Emilie Morvant,Amaury Habrard,Marc Sebban,Younès Bennani

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

Average ratings

N/A

5

0%

4

0%

3

0%

2

0%

1

0%

Solve jigsaw puzzle

Age

Page Count

210

Publisher

Elsevier

Published Date

ISBN 10

0081023472

ISBN 13

9780081023471