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    ESANN
    2003
    96views Neural Networks» more  ESANN 2003»
    Semi-automatic acquisition and labelling of image data using SOMs
    13 years 9 months ago
    Semi-automatic acquisition and labelling of image data using SOMs
    Download  www.dice.ucl.ac.be
    Abstract. Application of neural networks for real world object recognition suffers from the need to acquire large quantities of labelled image data. We propose a solution that acq...
    Gunther Heidemann, Axel Saalbach, Helge Ritter
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    ESANN
    2003
    124views Neural Networks» more  ESANN 2003»
    Neural networks organizations to learn complex robotic functions
    13 years 9 months ago
    Neural networks organizations to learn complex robotic functions
    Download  www.dice.ucl.ac.be
    Abstract. This paper considers the general problem of function estimation with a modular approach of neural computing. We propose to use functionally independent subnetworks to lea...
    Gilles Hermann, Patrice Wira, Jean-Philippe Urban
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    ESANN
    2003
    96views Neural Networks» more  ESANN 2003»
    Ensemble Methods for Multilayer Feedforward
    13 years 9 months ago
    Ensemble Methods for Multilayer Feedforward
    Download  www.dice.ucl.ac.be
    Training an ensemble of networks is an interesting way to improve the performance with respect to a single network. However there are several method to construct the ensemble and t...
    Carlos Hernández-Espinosa, Mercedes Fern&aa...
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    ESANN
    2003
    100views Neural Networks» more  ESANN 2003»
    Robust Vector Quantization for Burst Error Channels Using Genetic Algorithm
    13 years 9 months ago
    Robust Vector Quantization for Burst Error Channels Using Genetic Algorithm
    Download  www.dice.ucl.ac.be
    This paper presents a novel vector quantizer (VQ) design algorithm for a burst error channel (BEC). The algorithm minimizes the average distortion when the BEC is in normal state o...
    Wen-Jyi Hwang, Chien-Min Ou, Chin-Ming Yeh
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    ESANN
    2003
    134views Neural Networks» more  ESANN 2003»
    Autonomous learning algorithm for fully connected recurrent networks
    13 years 9 months ago
    Autonomous learning algorithm for fully connected recurrent networks
    Download  www.univ-lehavre.fr
    In this paper fully connected RTRL neural networks are studied. In order to learn dynamical behaviours of linear-processes or to predict time series, an autonomous learning algori...
    Edouard Leclercq, Fabrice Druaux, Dimitri Lefebvre
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