Yu Lixin (62 results)

Condition: Used. pp. 56 Index.

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Condition: Used. pp. 56.

Condition: Used. pp. 56.

Hardcover. Condition: New. Next day dispatch (mon-fri). Please note orders sent to Netherlands or Sweden take slightly longer than the Amazon estimated delivery date. Same day/next day dispatch (mon-fri) all items checked before dispatch, Handled and sent out with care.

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Kartoniert / Broschiert. Condition: New.

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Paperback. Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New. In English.

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Condition: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR.This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, andneural network. Additionally, domain knowledge in FinTech and marketing would be helpful.'.…

Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book constitutes the refereed proceedings of the First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, held in Vienna, Austria, during July 23-25, 2022. The 11 full papers presented in this book were carefully reviewed and selected from 12 submissions. They are organized in three topical sections: answer set programming; adaptive expert models for personalization in federated learning and privacy-preserving federated cross-domain social recommendation.…

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Taschenbuch. Condition: Neu. Federated Learning | Privacy and Incentive | Qiang Yang (u. a.) | Taschenbuch | Lecture Notes in Computer Science | x | Englisch | 2020 | Springer | EAN 9783030630751 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Paperback. Condition: Brand New. 286 pages. 9.25x6.10x0.55 inches. In Stock.

Original-Pappband. Condition: Sehr gut. gr8 Original-Pappband en VII pp., 204 pp.

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Condition: New. pp. 482.

Condition: New. In English.

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Hardback. Condition: New. 1st. This book gives an overview of the theoretical research on rogue waves and discusses solutions to rogue wave formation via the Darboux and bilinear transformations, algebro-geometric reduction, and inverse scattering and similarity transformations. Studies on nonlinear optics are included, making the book a comprehensive reference for researchers in applied mathematics, optical physics, geophysics, and ocean engineering. ContentsThe Research Process for Rogue WavesConstruction of Rogue Wave Solution by the Generalized Darboux TransformationConstruction of Rogue Wave Solution by Hirota Bilinear Method, Algebro-geometric Approach and Inverse Scattering MethodThe Rogue Wave Solution and Parameters Managing in Nonautonomous Physical Model.…

Condition: New. pp. 482.

Condition: New.

Condition: New. pp. 210.