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    ESANN
    2006
    179views Neural Networks» more  ESANN 2006»
    Visual nonlinear discriminant analysis for classifier design
    13 years 11 months ago
    Visual nonlinear discriminant analysis for classifier design
    Download  www.kecl.ntt.co.jp
    Abstract. We present a new method for analyzing classifiers by visualization, which we call visual nonlinear discriminant analysis. Classifiers that output posterior probabilities ...
    Tomoharu Iwata, Kazumi Saito, Naonori Ueda
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    ESANN
    2006
    170views Neural Networks» more  ESANN 2006»
    Discriminacy of the minimum range approach to blind separation of bounded sources
    13 years 11 months ago
    Discriminacy of the minimum range approach to blind separation of bounded sources
    Download  ljk.imag.fr
    The Blind Source Separation (BSS) problem is often solved by maximizing objective functions reflecting the statistical dependency between outputs. Since global maximization may be ...
    Dinh-Tuan Pham, Frédéric Vrins
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    ESANN
    2006
    599views Neural Networks» more  ESANN 2006»
    Learning Visual Invariance
    13 years 11 months ago
    Learning Visual Invariance
    Download  www.dice.ucl.ac.be
    Invariance is a necessary feature of a visual system able to recognize real objects in all their possible appearance. It is also the processing step most problematic to understand ...
    Alessio Plebe
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    ESANN
    2006
    108views Neural Networks» more  ESANN 2006»
    Rotation-based ensembles of RBF networks
    13 years 11 months ago
    Rotation-based ensembles of RBF networks
    Download  www.dice.ucl.ac.be
    Abstract. Ensemble methods allow to improve the accuracy of classification methods. This work considers the application of one of these methods, named Rotation-based, when the clas...
    Juan José Rodríguez, Jesús Ma...
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    ESANN
    2006
    85views Neural Networks» more  ESANN 2006»
    Synthesis of maximum margin and multiview learning using unlabeled data
    13 years 11 months ago
    Synthesis of maximum margin and multiview learning using unlabeled data
    Download  www.dice.ucl.ac.be
    In this presentation we show the semi-supervised learning with two input sources can be transformed into a maximum margin problem to be similar to a binary SVM. Our formulation exp...
    Sándor Szedmák, John Shawe-Taylor
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