Advances in Neural Networks - ISNN 2019, 1st ed. 2019 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10-12, 2019, Proceedings, Part II Theoretical Computer Science and General Issues Series
Coordonnateurs : Lu Huchuan, Tang Huajin, Wang Zhanshan
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.
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.
Date de parution : 06-2019
Ouvrage de 615 p.
15.5x23.5 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 52,74 €
Ajouter au panierThèmes d’Advances in Neural Networks - ISNN 2019 :
Mots-clés :
adaptive control systems; artificial intelligence; classification; data mining; estimation; evolutionary algorithms; genetic algorithms; image processing; image reconstruction; image segmentation; imaging systems; learning algorithms; linear matrix inequalities; matrix algebra; neural networks; numerical methods; process control; signal detection; signal processing; Support Vector Machines (SVM)
