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    ESANN
    2008
    117views Neural Networks» more  ESANN 2008»
    Inverting hyperspectral images with Gaussian Regularized Sliced Inverse Regression
    13 years 5 months ago
    Inverting hyperspectral images with Gaussian Regularized Sliced Inverse Regression
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    Caroline Bernard-Michel, Sylvain Douté, Lau...
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    ESANN
    2008
    142views Neural Networks» more  ESANN 2008»
    Simulation of a recurrent neurointerface with sparse electrical connections
    13 years 6 months ago
    Simulation of a recurrent neurointerface with sparse electrical connections
    Download  www.dice.ucl.ac.be
    With the technical development of multi-electrode arrays, the monitoring of many individual neurons has become feasible. However, for practical use of those arrays as bidirectional...
    Andreas Herzog, Karsten Kube, Bernd Michaelis, Ana...
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    ESANN
    2008
    111views Neural Networks» more  ESANN 2008»
    Feature Selection in Proton Magnetic Resonance Spectroscopy for Brain Tumor Classification
    13 years 6 months ago
    Feature Selection in Proton Magnetic Resonance Spectroscopy for Brain Tumor Classification
    Download  www.dice.ucl.ac.be
    Félix Fernando González-Navarro, Llu...
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    ESANN
    2008
    112views Neural Networks» more  ESANN 2008»
    K-nearest neighbours based on mutual information for incomplete data classification
    13 years 6 months ago
    K-nearest neighbours based on mutual information for incomplete data classification
    Download  www.dice.ucl.ac.be
    Incomplete data is a common drawback that machine learning techniques need to deal with when solving real-life classification tasks. One of the most popular procedures for solving ...
    Pedro J. García-Laencina, José-Luis ...
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    ESANN
    2008
    205views Neural Networks» more  ESANN 2008»
    An accelerated MDM algorithm for SVM training
    13 years 6 months ago
    An accelerated MDM algorithm for SVM training
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    In this work we will propose an acceleration procedure for the Mitchell
    Álvaro Barbero Jiménez, Jorge L&oacu...
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    ESANN
    2008
    127views Neural Networks» more  ESANN 2008»
    Neural networks for computational neuroscience
    13 years 6 months ago
    Neural networks for computational neuroscience
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    Computational neuroscience is an appealing interdisciplinary domain, at the interface between biology and computer science. It aims at understanding the experimental data obtained...
    David Meunier, Hélène Paugam-Moisy
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    ESANN
    2008
    125views Neural Networks» more  ESANN 2008»
    Methodology and standards for data analysis with machine learning tools
    13 years 6 months ago
    Methodology and standards for data analysis with machine learning tools
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    Many tools for data mining are complex and require skills and experience to be used successfully. Therefore, data mining is often considered an art as much as science. This paper p...
    Damien François
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    ESANN
    2008
    136views Neural Networks» more  ESANN 2008»
    Generalized matrix learning vector quantizer for the analysis of spectral data
    13 years 6 months ago
    Generalized matrix learning vector quantizer for the analysis of spectral data
    Download  www.dice.ucl.ac.be
    The analysis of spectral data constitutes new challenges for machine learning algorithms due to the functional nature of the data. Special attention is paid to the metric used in t...
    Petra Schneider, Frank-Michael Schleif, Thomas Vil...
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    ESANN
    2008
    111views Neural Networks» more  ESANN 2008»
    Metric adaptation for supervised attribute rating
    13 years 6 months ago
    Metric adaptation for supervised attribute rating
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    A new approach for faithful relevance rating of attributes is proposed, enabling class-specific discriminatory data space transformations. The method is based on the adaptation of ...
    Marc Strickert, Frank-Michael Schleif, Thomas Vill...
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    ESANN
    2008
    189views Neural Networks» more  ESANN 2008»
    Approximation of Gaussian process regression models after training
    13 years 6 months ago
    Approximation of Gaussian process regression models after training
    Download  www.neuroinformatik.ruhr-uni-bochum.de
    The evaluation of a standard Gaussian process regression model takes time linear in the number of training data points. In this paper, the models are approximated in the feature sp...
    Thorsten Suttorp, Christian Igel
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