Advances in Domain Adaptation Theory

Ievgen Redko

ISBN 10: 178548236X / ISBN 13: 9781785482366

Editore: Elsevier Science & Technology, ISTE Press - Elsevier, 2019

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nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Codice articolo 9781785482366

Dati bibliografici

• Titolo: Advances in Domain Adaptation Theory
• Editore: Elsevier Science & Technology, ISTE Press - Elsevier
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 178548236X
• ISBN 13: 9781785482366
• Rilegatura: Buch
• Condizione: Neu

Descrizione articolo

Riassunto

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

Informazioni sugli autori

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.

Amaury Habrard is a full professor at the Jean Monnet of Saint-Etienne University (UJM), he is also a member of the CNRS and the Computer Science department of UJM. He obtained his PhD in 2004 at the University of Saint-Etienne and his habilitation thesis in 2010.

Marc Sebban is a professor at the University of Jean Monnet of Saint-Etienne since 2001. He obtained his accreditation to lead research in 2001 and his PhD in 1996.

Younès Bennani obtained his PhD in 1992, and his accreditation to lead research in 1998. Dr. Younès Bennani joined the Computer Science Laboratory of Paris-Nord (LIPN-CNRS) at Paris 13 University in 1993.