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Artificial Neural Networks and Machine Learning - ICANN 2011

21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II

  • Conference proceedings
  • © 2011

Overview

Editors:
  1. Timo Honkela

    1. Department of Information and Computer Science, Aalto University School of Science, Aalto, Finland

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  2. Włodzisław Duch

    1. School of Physics, Astronomy and Informatics, Department of Informatics, Nicolaus Copernicus University, Torun, Poland

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  3. Mark Girolami

    1. Department of Statistical Science, University College London, London, UK

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  4. Samuel Kaski

    1. Department of Information and Computer Science, Aalto University School of Science, Aalto, Finland

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 6792)

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

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Conference proceedings info: ICANN 2011.

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About this book

This two volume set (LNCS 6791 and LNCS 6792) constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011.
The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.

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Table of contents (59 papers)

  1. Front Matter

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  2. A Markov Random Field Approach to Neural Encoding and Decoding

    • Marcel A. J. van Gerven, Eric Maris, Tom Heskes
    Pages 1-8

  3. Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs

    • Nicolas Heess, Nicolas Le Roux, John Winn
    Pages 9-16

  4. Recursive Multi-Way PLS for Adaptive Calibration of Brain Computer Interface System

    • Andrey Eliseyev, Alim-Louis Benabid, Tatiana Aksenova
    Pages 17-24

  5. Transformation of Edge Weights in a Graph Bipartitioning Problem

    • I. M. Karandashev, B. V. Kryzhanovsky
    Pages 25-31

  6. A Distributed Behavioral Model Using Neural Fields

    • Mohamed Oubbati, Josef Frick, Günther Palm
    Pages 32-39

  7. A Hypothetical Free Synaptic Energy Function and Related States of Synchrony

    • Karim El-Laithy, Martin Bogdan
    Pages 40-47

  8. Observational Learning Based on Models of Overlapping Pathways

    • Emmanouil Hourdakis, Panos Trahanias
    Pages 48-55

  9. On the Capacity of Transient Internal States in Liquid-State Machines

    • Karim El-Laithy, Martin Bogdan
    Pages 56-63

  10. Hybrid Parallel Classifiers for Semantic Subspace Learning

    • Nandita Tripathi, Michael Oakes, Stefan Wermter
    Pages 64-70

  11. Temperature Prediction in Electric Arc Furnace with Neural Network Tree

    • Mirosław Kordos, Marcin Blachnik, Tadeusz Wieczorek
    Pages 71-78

  12. Optimizing Linear Discriminant Error Correcting Output Codes Using Particle Swarm Optimization

    • Dimitrios Bouzas, Nikolaos Arvanitopoulos, Anastasios Tefas
    Pages 79-86

  13. SOS-HMM: Self-Organizing Structure of Hidden Markov Model

    • Rakia Jaziri, Mustapha Lebbah, Younès Bennani, Jean-Hugues Chenot
    Pages 87-94

  14. Image Receptive Fields Neural Networks for Object Recognition

    • Paméla Daum, Jean-Luc Buessler, Jean-Philippe Urban
    Pages 95-102

  15. A Comparison of the Electric Potential through the Membranes of Ganglion Neurons and Neuroblastoma Cells

    • Thiago M. Pinto, Roseli S. Wedemann, Célia Cortez
    Pages 103-110

  16. SNPboost: Interaction Analysis and Risk Prediction on GWA Data

    • Ingrid Brænne, Jeanette Erdmann, Amir Madany Mamlouk
    Pages 111-118

  17. Binary Patterns Identification by Vector Neural Network with Measure of Proximity between Neuron States

    • Vladimir Kryzhanovskiy
    Pages 119-126

  18. Emerging Bayesian Priors in a Self-Organizing Recurrent Network

    • Andreea Lazar, Gordon Pipa, Jochen Triesch
    Pages 127-134

  19. Momentum Acceleration of Least–Squares Support Vector Machines

    • Jorge López, Álvaro Barbero, José R. Dorronsoro
    Pages 135-142

  20. Fast Support Vector Training by Newton’s Method

    • Shigeo Abe
    Pages 143-150
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Other volumes

  1. Artificial Neural Networks and Machine Learning – ICANN 2011

  2. Artificial Neural Networks and Machine Learning – ICANN 2011

Editors and Affiliations

  • Department of Information and Computer Science, Aalto University School of Science, Aalto, Finland

    Timo Honkela, Samuel Kaski

  • School of Physics, Astronomy and Informatics, Department of Informatics, Nicolaus Copernicus University, Torun, Poland

    Włodzisław Duch

  • Department of Statistical Science, University College London, London, UK

    Mark Girolami

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