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Proceedings of ELM 2022 : Theory, Algorithms and Applications / edited by Kaj-Mikael Björk

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Proceedings in Adaptation, Learning and Optimization ; 18Publisher: Cham : Springer Nature Switzerland, 2024Publisher: Cham : Imprint: Springer, 2024Edition: 1st ed. 2024Description: 1 Online-Ressource(V, 81 p. 32 illus., 31 illus. in color.)ISBN:
  • 9783031550560
Subject(s): Additional physical formats: 9783031550553 | 9783031550577 | 9783031550584 | Erscheint auch als: 9783031550553 Druck-Ausgabe | Erscheint auch als: 9783031550577 Druck-Ausgabe | Erscheint auch als: 9783031550584 Druck-AusgabeDOI: DOI: 10.1007/978-3-031-55056-0Online resources: Summary: Application of ELM model to the motion detection of vehicles under moving background -- Distributed memory-efficient algorithm for Extreme Learning Machines based on Spark -- Does streaming affect video game popularity? -- Massive Offline Signature Forgery Detection with Extreme Learning Machines -- Importance of the Activation Function in Extreme Learning Machine for Acid Sulfate Soil Classification.Summary: This book contains selected papers from the 12th International Conference on Extreme Learning Machines 2022. Extreme learning machines (ELMs) continue to be an important complement to the many deep learning models you can find in the machine learning domain. ELM is fast and therefore suitable for many applications (not only in edge computing), and therefore there is a need to gather examples of possible applications. These proceedings, for the ELM 2022 conference, cover several application areas with relevant topics, where ELM can be used and has been used with great success. Here you will find several new areas (gaming, for instance) as well as improved concepts for existing application areas (signature forgery, for instance), where ELM has been implemented. In addition, some method improvements are also covered in this book, more specifically on the topic of 2nd-order Ordinary Differential Equations (ODEs).PPN: PPN: 1884355781Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-INR | ZDB-2-SXIT
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