Artificial Neural Networks and Machine Learning -- ICANN 2013

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

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.

• computational neuroscience
• distributed learning
• evolving systems
• natural language processing
• turing machines
• algorithm analysis and problem complexity

• 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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