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    ESANN
    2008
    103views Neural Networks» more  ESANN 2008»
    Selection of important input variables for RBF network using partial derivatives
    13 years 11 months ago
    Selection of important input variables for RBF network using partial derivatives
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    In regression problems, making accurate predictions is often the primary goal. Also, relevance of inputs in the prediction of an output would be valuable information in many cases....
    Jarkko Tikka, Jaakko Hollmén
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    ESANN
    2008
    163views Neural Networks» more  ESANN 2008»
    Clustering of Self-Organizing Map
    13 years 11 months ago
    Clustering of Self-Organizing Map
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    Abstract. In this paper, we present a new similarity measure for a clustering self-organizing map which will be reached using a new approach of hierarchical clustering. (1) The sim...
    Hanane Azzag, Mustapha Lebbah
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    ESANN
    2008
    150views Neural Networks» more  ESANN 2008»
    Conditional prediction of time series using spiral recurrent neural network
    13 years 11 months ago
    Conditional prediction of time series using spiral recurrent neural network
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    Frequently, sequences of state transitions are triggered by specific signals. Learning these triggered sequences with recurrent neural networks implies storing them as different at...
    Huaien Gao, Rudolf Sollacher
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    ESANN
    2008
    149views Neural Networks» more  ESANN 2008»
    Detecting zebra crossings utilizing AdaBoost
    13 years 11 months ago
    Detecting zebra crossings utilizing AdaBoost
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    This paper introduces a visual zebra crossing detector based on the Viola-Jones approach. The basic properties of this cascaded classifier and the use of integral images are explai...
    Ludwig Lausser, Friedhelm Schwenker, Günther ...
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    ESANN
    2008
    133views Neural Networks» more  ESANN 2008»
    Linear Projection based on Noise Variance Estimation - Application to Spectral Data
    13 years 11 months ago
    Linear Projection based on Noise Variance Estimation - Application to Spectral Data
    Download  www.dice.ucl.ac.be
    In this paper, we propose a new methodology to build latent variables that are optimal if a nonlinear model is used afterward. This method is based on Nonparametric Noise Estimatio...
    Amaury Lendasse, Francesco Corona
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    ESANN
    2008
    137views Neural Networks» more  ESANN 2008»
    Classification of chestnuts with feature selection by noise resilient classifiers
    13 years 11 months ago
    Classification of chestnuts with feature selection by noise resilient classifiers
    Download  www.dice.ucl.ac.be
    In this paper we solve the problem of classifying chestnut plants according to their place of origin. We compare the results obtained by state of the art classifiers, among which,...
    Elena Roglia, Rossella Cancelliere, Rosa Meo
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    ESANN
    2008
    91views Neural Networks» more  ESANN 2008»
    Phase transitions in Vector Quantization
    13 years 11 months ago
    Phase transitions in Vector Quantization
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    Abstract. We study Winner-Takes-All and rank based Vector Quantization along the lines of the statistical physics of off-line learning. Typical behavior of the system is obtained w...
    Aree Witoelar, Anarta Ghosh, Michael Biehl
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    ESANN
    2008
    121views Neural Networks» more  ESANN 2008»
    A neural model with feedback for robust disambiguation of motion
    13 years 11 months ago
    A neural model with feedback for robust disambiguation of motion
    Download  www.dice.ucl.ac.be
    Abstract. The aperture problem is a direct consequence of any local detection in the visual perception of motion. It results in ambiguous responses of the local motion detectors. B...
    Mauricio Cerda, Bernard Girau
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    ESANN
    2008
    108views Neural Networks» more  ESANN 2008»
    Neural network hardware architecture for pattern recognition in the HESS2 project
    13 years 11 months ago
    Neural network hardware architecture for pattern recognition in the HESS2 project
    Download  www.dice.ucl.ac.be
    In this paper, we consider the problem of implementation of neural network in the context of the level 2 trigger of HESS2 project. We propose a hardware architecture which which ta...
    Narayanan Ramanan, Sonia Khatchadourian, Jean-Chri...
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    ESANN
    2008
    90views Neural Networks» more  ESANN 2008»
    Comparison of sparse least squares support vector regressors trained in primal and dual
    13 years 11 months ago
    Comparison of sparse least squares support vector regressors trained in primal and dual
    Download  www.dice.ucl.ac.be
    In our previous work, we have developed sparse least squares support vector regressors (sparse LS SVRs) trained in the primal form in the reduced empirical feature space. In this p...
    Shigeo Abe
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