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Artificial Neural Networks and Machine Learning -- ICANN 2013

23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 10-13, 2013, Proceedings

  • Conference proceedings
  • © 2013

Overview

Editors:
  1. Valeri Mladenov

    1. Faculty Automation,, Technical University of Sofia, Sofia, Bulgaria

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  2. Petia Koprinkova-Hristova

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

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  3. Günther Palm

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

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  4. Alessandro E. P. Villa

    1. Quartier UNIL-Dorigny, Bâtiment Internef, Université de Lausanne, Lausanne, Switzerland

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  5. Bruno Appollini

    1. Department of Computer Science, University of Milano, Milano, Italy

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  6. Nikola Kasabov

    1. Knowledge Engineering, School of Computing and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand

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  • Fast track conference proceedings of ICANN 2013

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8131)

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

Included in the following conference series:

Conference proceedings info: ICANN 2013.

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Table of contents (78 papers)

  1. Front Matter

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  2. Neural Network Theory and Models

    1. Hessian Corrected Input Noise Models

      • Botond Attila Bócsi, Lehel Csató
      Pages 1-8

    2. Model-Based Clustering of Temporal Data

      • Hani El Assaad, Allou Samé, Gérard Govaert, Patrice Aknin
      Pages 9-16

    3. Fast Approximation Method for Gaussian Process Regression Using Hash Function for Non-uniformly Distributed Data

      • Yuya Okadome, Yutaka Nakamura, Yumi Shikauchi, Shin Ishii, Hiroshi Ishiguro
      Pages 17-25

    4. An Analytical Approach to Single Node Delay-Coupled Reservoir Computing

      • Johannes Schumacher, Hazem Toutounji, Gordon Pipa
      Pages 26-33

    5. Applying General-Purpose Data Reduction Techniques for Fast Time Series Classification

      • Stefanos Ougiaroglou, Leonidas Karamitopoulos, Christos Tatoglou, Georgios Evangelidis, Dimitris A. Dervos
      Pages 34-41

    6. Two-Layer Vector Perceptron

      • Vladimir Kryzhanovsky, Irina Zhelavskaya, Juan Antonio Clares Tomas
      Pages 42-49

    7. Local Detection of Communities by Neural-Network Dynamics

      • Hiroshi Okamoto
      Pages 50-57

    8. The Super-Turing Computational Power of Interactive Evolving Recurrent Neural Networks

      • Jérémie Cabessa, Alessandro E. P. Villa
      Pages 58-65

    9. Group Fused Lasso

      • Carlos M. Alaíz, Álvaro Barbero, José R. Dorronsoro
      Pages 66-73

    10. Exponential Synchronization of a Class of RNNs with Discrete and Distributed Delays

      • Farouk Chérif, Hajer Brahmi, Boudour Ammar, Adel M. Alimi
      Pages 74-81

    11. Variational Foundations of Online Backpropagation

      • Salvatore Frandina, Marco Gori, Marco Lippi, Marco Maggini, Stefano Melacci
      Pages 82-89

    12. GNMF with Newton-Based Methods

      • Rafał Zdunek, Anh-Huy Phan, Andrzej Cichocki
      Pages 90-97

    13. Improving the Associative Rule Chaining Architecture

      • Nathan Burles, Simon O’Keefe, James Austin
      Pages 98-105

  3. Machine Learning and Learning Algorithms

    1. A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines

      • KyungHyun Cho, Tapani Raiko, Alexander Ilin, Juha Karhunen
      Pages 106-113

    2. A Low-Energy Implementation of Finite Automata by Optimal-Size Neural Nets

      • Jiří Šíma
      Pages 114-121

    3. A Distributed Learning Algorithm Based on Frontier Vector Quantization and Information Theory

      • Diego Peteiro-Barral, Bertha Guijarro-Berdiñas
      Pages 122-129

    4. Efficient Baseline-Free Sampling in Parameter Exploring Policy Gradients: Super Symmetric PGPE

      • Frank Sehnke
      Pages 130-137

    5. Direct Method for Training Feed-Forward Neural Networks Using Batch Extended Kalman Filter for Multi-Step-Ahead Predictions

      • Artem Chernodub
      Pages 138-145

    6. Learning with Hard Constraints

      • Giorgio Gnecco, Marco Gori, Stefano Melacci, Marcello Sanguineti
      Pages 146-153
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  1. Artificial Neural Networks and Machine Learning – ICANN 2013

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About this book

The book constitutes the proceedings of the 23rd International Conference on Artificial Neural Networks, ICANN 2013, held in Sofia, Bulgaria, in September 2013. The 78 papers included in the proceedings were carefully reviewed and selected from 128 submissions. The focus of the papers is on following topics: neurofinance graphical network models, brain machine interfaces, evolutionary neural networks, neurodynamics, complex systems, neuroinformatics, neuroengineering, hybrid systems, computational biology, neural hardware, bioinspired embedded systems, and collective intelligence.

Editors and Affiliations

  • Faculty Automation,, Technical University of Sofia, Sofia, Bulgaria

    Valeri Mladenov

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

    Petia Koprinkova-Hristova

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

    Günther Palm

  • Quartier UNIL-Dorigny, Bâtiment Internef, Université de Lausanne, Lausanne, Switzerland

    Alessandro E. P. Villa

  • Department of Computer Science, University of Milano, Milano, Italy

    Bruno Appollini

  • Knowledge Engineering, School of Computing and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand

    Nikola Kasabov

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