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Artificial Neural Networks and Machine Learning – ICANN 2014

24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings

  • Stefan Wermter
  • Cornelius Weber
  • Włodzisław Duch
  • Timo Honkela
  • Petia Koprinkova-Hristova
  • Sven Magg
  • Günther Palm
  • Alessandro E. P. Villa

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Table of contents

  1. Vision

    1. Detection and Recognition

      1. Pablo Barros, Sven Magg, Cornelius Weber, Stefan Wermter
        Pages 403-410
      2. Marvin Struwe, Stephan Hasler, Ute Bauer-Wersing
        Pages 411-418
      3. Hanchen Xiong, Sandor Szedmak, Antonio Rodríguez-Sánchez, Justus Piater
        Pages 419-426
    2. Invariances and Shape Recovery

    3. Attention and Pose Estimation

      1. Oliver Lomp, Kasim Terzić, Christian Faubel, J. M. H. du Buf, Gregor Schöner
        Pages 451-458
      2. Frederik Beuth, Amirhossein Jamalian, Fred H. Hamker
        Pages 459-466
      3. Yoshihiro Nagano, Norifumi Watanabe, Atsushi Aoyama
        Pages 467-474
  2. Supervised Learning

    1. Ensembles

    2. Regression

      1. Iveta Mrázová, Zuzana Petříčková
        Pages 507-514
      2. Fabio Aiolli, Michele Donini
        Pages 515-522
      3. Bassam A. Almogahed, Ioannis A. Kakadiaris
        Pages 523-530
      4. Luiz C. B. Torres, André P. Lemos, Cristiano L. Castro, Antônio P. Braga
        Pages 531-538
      5. Sho Sonoda, Noboru Murata
        Pages 539-546
    3. Classification

      1. Shasha Wang, Liangxiao Jiang, Chaoqun Li
        Pages 555-562
      2. Lydia Fischer, Barbara Hammer, Heiko Wersing
        Pages 563-570
      3. Bassam Mokbel, Benjamin Paassen, Barbara Hammer
        Pages 571-578
      4. César Lincoln C. Mattos, José Daniel A. Santos, Guilherme A. Barreto
        Pages 579-586
      5. Fadi Dornaika, Alireza Bosaghzadeh
        Pages 595-602
  3. Dynamical Models and Time Series

    1. Dmytro Velychko, Dominik Endres, Nick Taubert, Martin A. Giese
      Pages 603-610
    2. Jorge Dávila-Chacón, Johannes Twiefel, Jindong Liu, Stefan Wermter
      Pages 619-626
    3. Amine M. Khelifa, Abdelkrim Boukabou
      Pages 627-634
    4. Tingting (Amy) Gibson, Scott Heath, Robert P. Quinn, Alexia H. Lee, Joshua T. Arnold, Tharun S. Sonti et al.
      Pages 635-642
    5. Yancho Todorov, Margarita Terziyska
      Pages 643-650
  4. Neuroscience

    1. Cortical Models

      1. Daniel Malagarriga, Alessandro E. P. Villa, Jordi García-Ojalvo, Antonio J. Pons
        Pages 651-658
      2. Alessandra M. Soares, Bruno J. T. Fernandes, Carmelo J. A. Bastos-Filho
        Pages 667-674
      3. Jan Kneissler, Martin V. Butz
        Pages 683-690
    2. Line Attractors and Neural Fields

      1. Mohsen Firouzi, Stefan Glasauer, Jörg Conradt
        Pages 691-698
      2. Christian Bell, Tobias Storck, Yulia Sandamirskaya
        Pages 699-706
    3. Spiking and Single Cell Models

      1. Panagiotis Ioannou, Matthew Casey, André Grüning
        Pages 723-731
      2. Youwei Zheng, Lars Schwabe, Joshua L. Plotkin
        Pages 741-748
      3. Brian Gardner, Ioana Sporea, André Grüning
        Pages 749-756
  5. Applications

    1. Users and Social Technologies

      1. Henri Sintonen, Juha Raitio, Timo Honkela
        Pages 757-764
      2. Yoshitatsu Matsuda, Kazunori Yamaguchi, Ken-ichiro Nishioka
        Pages 765-772
      3. Yongqi Liu, Qiuli Tong, Zhao Du, Lantao Hu
        Pages 773-780
      4. Marina Resta
        Pages 781-788
    2. Technical Systems

      1. Stefan Müller, Cornelius Weber, Stefan Wermter
        Pages 789-796

About these proceedings

Introduction

The book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014.
The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory; reinforcement learning and action; vision; supervised learning; dynamical models and time series; neuroscience; and applications.

Keywords

computational neuroscience distributed computation dynamical systems ensemble methods evolving systems machine learning neural networks parallel distributed system particle swarm optimization reinforcement learning robust pattern recognition self-organizing maps speech recognition support vector machines swarm intelligence turing machines unsupervised learning

Editors and affiliations

  • Stefan Wermter
    • 1
  • Cornelius Weber
    • 1
  • Włodzisław Duch
    • 2
  • Timo Honkela
    • 3
  • Petia Koprinkova-Hristova
    • 4
  • Sven Magg
    • 1
  • Günther Palm
    • 5
  • Alessandro E. P. Villa
    • 6
  1. 1.Department of InformaticsUniversity of HamburgHamburgGermany
  2. 2.Department of InformaticsNicolaus Compernicus UniversityTorunPoland
  3. 3.Department of Modern LanguagesUniversity of HelsinkiHelsinkiFinland
  4. 4.Institute of Information and Communication TechnologiesBulgarian Academy of SciencesSofiaBulgaria
  5. 5.Institute of Neural Information ProcessingUniversity of UlmOberer EselsbergGermany
  6. 6.Department of Information Systems, Quartier UNIL-Dorigny, Bâtiment InternefUniversity of LausanneLausanneSwitzerland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-11179-7
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-11178-0
  • Online ISBN 978-3-319-11179-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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