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© 2019

Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning

28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II

  • Igor V. Tetko
  • Věra Kůrková
  • Pavel Karpov
  • Fabian Theis
Conference proceedings ICANN 2019

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11728)

Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 11728)

Table of contents

  1. Front Matter
    Pages i-xxx
  2. Feature Selection

    1. Front Matter
      Pages 1-1
    2. Sijia Niu, Pengfei Zhu, Qinghua Hu, Hong Shi
      Pages 3-15
    3. Tirtharaj Dash, Ashwin Srinivasan, Ramprasad S. Joshi, A. Baskar
      Pages 29-45
    4. Yang Fan, Jianhua Dai, Qilai Zhang, Shuai Liu
      Pages 46-58
    5. Alexandra Degeest, Michel Verleysen, Benoît Frénay
      Pages 59-71
    6. Vadim Borisov, Johannes Haug, Gjergji Kasneci
      Pages 72-83
    7. Nicomedes L. Cavalcanti Jr., Marcelo Rodrigo Portela Ferreira, Francisco de Assis Tenorio de Carvalho
      Pages 84-95
  3. Augmentation Techniques

    1. Front Matter
      Pages 101-101
    2. Yuuji Ichisugi, Naoto Takahashi, Hidemoto Nakada, Takashi Sano
      Pages 103-114
    3. Ricardo Cruz, Joaquim F. Pinto Costa, Jaime S. Cardoso
      Pages 115-124
    4. Shizheng Qin, Kangzheng Gu, Lecheng Wang, Lizhe Qi, Wenqiang Zhang
      Pages 125-137
    5. Guanghua Tan, Zijun Guo, Yi Xiao
      Pages 138-149
  4. Weights Initialization

    1. Front Matter
      Pages 151-151
    2. Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh
      Pages 153-164
    3. Aiga Suzuki, Hidenori Sakanashi
      Pages 165-169
    4. Diego Aguirre, Olac Fuentes
      Pages 170-184
  5. Parameters Optimisation

    1. Front Matter
      Pages 185-185

Other volumes

  1. 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I
  2. Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning
    28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II
  3. 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III
  4. 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV
  5. 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings

About these proceedings

Introduction

The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. 

The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions. 

Keywords

artificial intelligence classification clustering computational linguistics computer networks Human-Computer Interaction (HCI) image processing image reconstruction image segmentation imaging systems learning algorithms machine learning neural networks recurrent neural networks robotics semantics sensors signal processing Support Vector Machines (SVM) user interfaces

Editors and affiliations

  1. 1.Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)NeuherbergGermany
  2. 2.Institute of Computer ScienceCzech Academy of SciencesPrague 8Czech Republic
  3. 3.Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)NeuherbergGermany
  4. 4.Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)NeuherbergGermany

Bibliographic information