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Advances in Neural Networks – ISNN 2019

16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I

  • Huchuan Lu
  • Huajin Tang
  • Zhanshan Wang
Conference proceedings ISNN 2019

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11554)

Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 11554)

Table of contents

  1. Model Optimization, Bayesian Learning, and Clustering

    1. Xin Song, Shizhen Qin, Shaokai Niu, Yan Wang
      Pages 444-453
    2. Hangjun Che, Jun Wang, Wei Zhang
      Pages 454-462
    3. Eduardo C. Simões, Francisco de A. T. de Carvalho
      Pages 469-477
  2. Back Matter
    Pages 479-483

Other volumes

  1. Advances in Neural Networks – ISNN 2019
    16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I
  2. 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part II

About these proceedings

Introduction

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.

 

Keywords

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)

Editors and affiliations

  • Huchuan Lu
    • 1
  • Huajin Tang
    • 2
  • Zhanshan Wang
    • 3
  1. 1.Dalian University of TechnologyDalianChina
  2. 2.Sichuan UniversityChengduChina
  3. 3.Northeastern UniversityShenyangChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-22796-8
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-22795-1
  • Online ISBN 978-3-030-22796-8
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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