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Neural Information Processing 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part III /

The three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 146 papers presented in the proceedings set were carefully reviewed and select...

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Körperschaft: SpringerLink (Online service)
Weitere Verfasser: Tanveer, Mohammad (HerausgeberIn), Agarwal, Sonali (HerausgeberIn), Ozawa, Seiichi (HerausgeberIn), Ekbal, Asif (HerausgeberIn), Jatowt, Adam (HerausgeberIn)
Format: Elektronisch E-Book
Sprache:Englisch
Veröffentlicht: Cham : Springer International Publishing : Imprint: Springer, 2023.
Ausgabe:1st ed. 2023.
Schriftenreihe:Lecture Notes in Computer Science, 13625
Schlagworte:
Pattern recognition systems.
Data mining.
Machine learning.
Social sciences > Data processing.
Automated Pattern Recognition.
Data Mining and Knowledge Discovery.
Machine Learning.
Computer Application in Social and Behavioral Sciences.
Online-Zugang:https://doi.org/10.1007/978-3-031-30111-7
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Beschreibung
Zusammenfassung:The three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.
Beschreibung:1 online resource (XXXVI, 722 p. 241 illus.). 222 illus. in color.
ISBN:9783031301117
ISSN:1611-3349 ;
DOI:10.1007/978-3-031-30111-7

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