Advances in Domain Adaptation Theory Advances in Domain Adaptation Theory

Advances in Domain Adaptation Theory

Ievgen Redko and Others
    • $124.99
    • $124.99

Publisher 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. 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

GENRE
Computers & Internet
RELEASED
2019
August 23
LANGUAGE
EN
English
LENGTH
208
Pages
PUBLISHER
Elsevier Science
SELLER
Elsevier Ltd.
SIZE
16.1
MB

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