Articles liés à Advances in Domain Adaptation Theory
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos des auteurs
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Advances in Domain Adaptation Theory: Available Theoretical Results
Edité par
Elsevier Science Ltd, 2019
ISBN 10 : 178548236X
ISBN 13 : 9781785482366
Neuf
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Advances in Domain Adaptation Theory
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ISTE Press - Elsevier, 2019
ISBN 10 : 178548236X
ISBN 13 : 9781785482366
Neuf
Couverture rigide
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Advances in Domain Adaptation Theory
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Elsevier Science & Technology, ISTE Press - Elsevier, 2019
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ISBN 13 : 9781785482366
Neuf
Couverture rigide
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. Englisch. N° de réf. du vendeur 9781785482366
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Advances in Domain Adaptation Theory
Edité par
ISTE Press Ltd - Elsevier Inc, 2019
ISBN 10 : 178548236X
ISBN 13 : 9781785482366
Neuf
Couverture rigide
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Advances in Domain Adaptation Theory
Edité par
Elsevier Science & Technology, ISTE Press - Elsevier, 2019
ISBN 10 : 178548236X
ISBN 13 : 9781785482366
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. 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. N° de réf. du vendeur 9781785482366
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Advances in Domain Adaptation Theory
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ISTE Press - Elsevier, 2019
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ISBN 13 : 9781785482366
Neuf
Couverture rigide
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Advances in Domain Adaptation Theory
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ISTE Press - Elsevier, 2019
ISBN 10 : 178548236X
ISBN 13 : 9781785482366
Neuf
Couverture rigide
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Advances in Domain Adaptation Theory
Edité par
ISTE Press - Elsevier, 2019
ISBN 10 : 178548236X
ISBN 13 : 9781785482366
Neuf
Couverture rigide
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Advances in Domain Adaptation Theory
Edité par
ISTE Press - Elsevier, 2019
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ISBN 13 : 9781785482366
Neuf
Couverture rigide
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Domain Adaptation Theory: Available Theoretical Results
Edité par
Elsevier Science & Technology|ISTE Press - Elsevier, 2019
ISBN 10 : 178548236X
ISBN 13 : 9781785482366
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
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Gebunden. Etat : New. 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 conc. N° de réf. du vendeur 257086532
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