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Published by ISTE Press - Elsevier, 2019
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Published by ISTE Press Ltd - Elsevier Inc, 2019
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Published by ISTE Press - Elsevier, 2019
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Published by ISTE Press - Elsevier, 2019
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Published by ISTE Press - Elsevier, 2019
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Published by Elsevier Science Ltd, 2019
ISBN 10: 178548236X ISBN 13: 9781785482366
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Published by Elsevier Science & Technology, ISTE Press - Elsevier, 2019
ISBN 10: 178548236X ISBN 13: 9781785482366
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Add to basketBuch. Condition: 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.
Published by ISTE Press Ltd - Elsevier Inc, 2019
ISBN 10: 178548236X ISBN 13: 9781785482366
Language: English
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Published by Elsevier Science & Technology, ISTE Press - Elsevier, 2019
ISBN 10: 178548236X ISBN 13: 9781785482366
Language: English
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Add to basketBuch. Condition: 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.