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Published by Springer Nature Switzerland, 2025
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Paperback. Condition: new. Paperback. This LNAI volume constitutes the post proceedings of International Federated Learning Workshops such as follows:FLatFM-WWW 2024, FLatFM-ICME 2024, FLatFM-IJCAI 2024 and FLatFM-NeurIPS 2024. This LNAI volume focuses on the following topics:Efficient Model Adaptation and Personalization, Data Heterogeneity and Incomplete Data, Integration of Specialized Neural Architectures, Frameworks and Tools for Federated Learning, Applications in Domain-Specific Contexts, Unsupervised and Lightweight Learning, and Causal Discovery and Black-Box Optimization. This LNAI volume constitutes the post proceedings of International Federated Learning Workshops such as follows:FLatFM-WWW 2024, FLatFM-ICME 2024, FLatFM-IJCAI 2024 and FLatFM-NeurIPS 2024. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Language: English
Published by LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843379106 ISBN 13: 9783843379106
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Published by LAP LAMBERT Academic Publishing 2010-12-28, 2010
ISBN 10: 3843379106 ISBN 13: 9783843379106
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Published by LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843379106 ISBN 13: 9783843379106
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Taschenbuch. Condition: Neu. More Than Semi-supervised Learning | A unified view on Learning with Labeled and Unlabeled Data | Zenglin Xu (u. a.) | Taschenbuch | 132 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783843379106 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.
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Taschenbuch. Condition: Neu. Federated Learning in the Age of Foundation Models - FL 2024 International Workshops | FL[at]FM-WWW 2024, Singapore, May 14, 2024; FL[at]FM-ICME 2024, Niagara Falls, ON, Canada, July 15, 2024; FL[at]FM-IJCAI 2024, Jeju Island, South Korea, August 5, 2024; and FL[at]FM-NeurIPS 2024, Vancouver, BC, Canada, December 15, 2024, Revised Select | Han Yu (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xii | Englisch | 2025 | Springer | EAN 9783031822391 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Published by Springer International Publishing AG, CH, 2025
ISBN 10: 3031822390 ISBN 13: 9783031822391
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Published by LAP LAMBERT Academic Publishing, 2010
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Published by LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843379106 ISBN 13: 9783843379106
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Published by LAP LAMBERT Academic Publishing, 2010
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Published by Springer, Springer Mär 2025, 2025
ISBN 10: 3031822390 ISBN 13: 9783031822391
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 196 pp. Englisch.
Language: English
Published by LAP LAMBERT Academic Publishing Dez 2010, 2010
ISBN 10: 3843379106 ISBN 13: 9783843379106
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Semi-supervised learning (SSL) has grown into an important research area in machine learning, motivated by the fact that human labeling is expensive while unlabeled data are relatively easy to obtain. A basic assumption in traditional SSL is that unlabeled data and labeled data share the same distribution. However, this assumption may be incorrect when unlabeled data have a shifted covariance, or come from a related but different domain, or contain irrelevant data. With the divergence of the distribution of unlabeled data, very little academic literature exists on how to choose or adapt machine learning algorithms to different settings of unlabeled data. This book, therefore, introduces a new unified view on learning with different settings of unlabeled data. This book consists of two parts: the first part analyzes the fundamental assumptions of SSL and proposes a few efficient SSL algorithms; the second part discusses three learning frameworks to deal with other settings of unlabeled data. This book should be helpful to researchers or graduate students in areas with abundance of unlabeled data, such as computer vision, bioinformatics, web mining, and natural language processing. 132 pp. Englisch.
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