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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

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. Stefan Wermter

    1. Department of Informatics, University of Hamburg, Hamburg, Germany

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  2. Cornelius Weber

    1. Department of Informatics, University of Hamburg, Hamburg, Germany

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  3. Włodzisław Duch

    1. Department of Informatics, Nicolaus Compernicus University, Torun, Poland

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  4. Timo Honkela

    1. Department of Modern Languages, University of Helsinki, Helsinki, Finland

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  5. Petia Koprinkova-Hristova

    1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

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  6. Sven Magg

    1. Department of Informatics, University of Hamburg, Hamburg, Germany

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  7. Günther Palm

    1. Institute of Neural Information Processing, University of Ulm, Oberer Eselsberg, Germany

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  8. Alessandro E. P. Villa

    1. Department of Information Systems, Quartier UNIL-Dorigny, Bâtiment Internef, University of Lausanne, Lausanne, Switzerland

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

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: ICANN 2014.

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Table of contents (105 papers)

  1. Front Matter

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  2. Recurrent Networks

    1. Sequence Learning

      1. Dynamic Cortex Memory: Enhancing Recurrent Neural Networks for Gradient-Based Sequence Learning
        • Sebastian Otte, Marcus Liwicki, Andreas Zell
        Pages 1-8
      2. Learning and Recognition of Multiple Fluctuating Temporal Patterns Using S-CTRNN
        • Shingo Murata, Hiroaki Arie, Tetsuya Ogata, Jun Tani, Shigeki Sugano
        Pages 9-16
      3. Regularized Recurrent Neural Networks for Data Efficient Dual-Task Learning
        • Sigurd Spieckermann, Siegmund Düll, Steffen Udluft, Thomas Runkler
        Pages 17-24

    3. Recurrent Network Theory

      1. Interactive Evolving Recurrent Neural Networks Are Super-Turing Universal
        • Jérémie Cabessa, Alessandro E. P. Villa
        Pages 57-64
      2. Attractor Metadynamics in Adapting Neural Networks
        • Claudius Gros, Mathias Linkerhand, Valentin Walther
        Pages 65-72
      3. Basic Feature Quantities of Digital Spike Maps
        • Hiroki Yamaoka, Narutoshi Horimoto, Toshimichi Saito
        Pages 73-80

  3. Competitive Learning and Self-Organisation

    1. Discriminative Fast Soft Competitive Learning

      • Frank-Michael Schleif
      Pages 81-88

    2. Human Action Recognition with Hierarchical Growing Neural Gas Learning

      • German Ignacio Parisi, Cornelius Weber, Stefan Wermter
      Pages 89-96

    3. Real-Time Anomaly Detection with a Growing Neural Gas

      • Nicolai Waniek, Simon Bremer, Jörg Conradt
      Pages 97-104

    4. Classification with Reject Option Using the Self-Organizing Map

      • Ricardo Sousa, Ajalmar R. da Rocha Neto, Jaime S. Cardoso, Guilherme A. Barreto
      Pages 105-112

  4. Clustering and Classification

    1. A Non-parametric Maximum Entropy Clustering

      • Hideitsu Hino, Noboru Murata
      Pages 113-120

    2. Instance Selection Using Two Phase Collaborative Neighbor Representation

      • Fadi Dornaika, I. Kamal Aldine
      Pages 121-128

    3. Global Metric Learning by Gradient Descent

      • Jens Hocke, Thomas Martinetz
      Pages 129-135

    4. Leaving Local Optima in Unsupervised Kernel Regression

      • Daniel Lückehe, Oliver Kramer
      Pages 137-144

  5. Trees and Graphs

    1. An Algorithm for Directed Graph Estimation

      • Hideitsu Hino, Atsushi Noda, Masami Tatsuno, Shotaro Akaho, Noboru Murata
      Pages 145-152
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About this book

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.

Editors and Affiliations

  • Department of Informatics, University of Hamburg, Hamburg, Germany

    Stefan Wermter, Cornelius Weber, Sven Magg

  • Department of Informatics, Nicolaus Compernicus University, Torun, Poland

    Włodzisław Duch

  • Department of Modern Languages, University of Helsinki, Helsinki, Finland

    Timo Honkela

  • Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Petia Koprinkova-Hristova

  • Institute of Neural Information Processing, University of Ulm, Oberer Eselsberg, Germany

    Günther Palm

  • Department of Information Systems, Quartier UNIL-Dorigny, Bâtiment Internef, University of Lausanne, Lausanne, Switzerland

    Alessandro E. P. Villa

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