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Advances in Neural Networks - ISNN 2019 [electronic resource] : 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10-12, 2019, Proceedings, Part I / edited by Huchuan Lu, Huajin Tang, Zhanshan Wang.

Contributor(s): Lu, Huchuan [editor.] | Tang, Huajin [editor.] | Wang, Zhanshan [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Theoretical Computer Science and General Issues: 11554Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: XXII, 483 p. 198 illus., 133 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030227968.Subject(s): Artificial intelligence | Computer vision | Numerical analysis | Data mining | Computer science -- Mathematics | Algorithms | Artificial Intelligence | Computer Vision | Numerical Analysis | Data Mining and Knowledge Discovery | Mathematical Applications in Computer Science | AlgorithmsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Learning System, Graph Model, and Adversarial Learning -- Time Series Analysis, Dynamic Prediction, and Uncertain Estimation -- Model Optimization, Bayesian Learning, and Clustering -- Game Theory, Stability Analysis, and Control Method -- Signal Processing, Industrial Application, and Data Generation -- Image Recognition, Scene Understanding, and Video Analysis -- Bio-signal, Biomedical Engineering, and Hardware.
In: Springer Nature eBookSummary: This two-volume set LNCS 11554 and 11555 constitutes the refereed proceedings of the 16th International Symposium on Neural Networks, ISNN 2019, held in Moscow, Russia, in July 2019. The 111 papers presented in the two volumes were carefully reviewed and selected from numerous submissions. The papers were organized in topical sections named: Learning System, Graph Model, and Adversarial Learning; Time Series Analysis, Dynamic Prediction, and Uncertain Estimation; Model Optimization, Bayesian Learning, and Clustering; Game Theory, Stability Analysis, and Control Method; Signal Processing, Industrial Application, and Data Generation; Image Recognition, Scene Understanding, and Video Analysis; Bio-signal, Biomedical Engineering, and Hardware. .
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Learning System, Graph Model, and Adversarial Learning -- Time Series Analysis, Dynamic Prediction, and Uncertain Estimation -- Model Optimization, Bayesian Learning, and Clustering -- Game Theory, Stability Analysis, and Control Method -- Signal Processing, Industrial Application, and Data Generation -- Image Recognition, Scene Understanding, and Video Analysis -- Bio-signal, Biomedical Engineering, and Hardware.

This two-volume set LNCS 11554 and 11555 constitutes the refereed proceedings of the 16th International Symposium on Neural Networks, ISNN 2019, held in Moscow, Russia, in July 2019. The 111 papers presented in the two volumes were carefully reviewed and selected from numerous submissions. The papers were organized in topical sections named: Learning System, Graph Model, and Adversarial Learning; Time Series Analysis, Dynamic Prediction, and Uncertain Estimation; Model Optimization, Bayesian Learning, and Clustering; Game Theory, Stability Analysis, and Control Method; Signal Processing, Industrial Application, and Data Generation; Image Recognition, Scene Understanding, and Video Analysis; Bio-signal, Biomedical Engineering, and Hardware. .

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