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    ESANN
    2004
    163views Neural Networks» more  ESANN 2004»
    Neural methods for non-standard data
    14 years 7 days ago
    Neural methods for non-standard data
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    Standard pattern recognition provides effective and noise-tolerant tools for machine learning tasks; however, most approaches only deal with real vectors of a finite and fixed dime...
    Barbara Hammer, Brijnesh J. Jain
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    ESANN
    2004
    87views Neural Networks» more  ESANN 2004»
    A VLSI reconfigurable network of integrate-and-fire neurons with spike-based learning synapses
    14 years 7 days ago
    A VLSI reconfigurable network of integrate-and-fire neurons with spike-based learning synapses
    Download  www.ini.uzh.ch
    Giacomo Indiveri, Elisabetta Chicca, Rodney J. Dou...
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    ESANN
    2004
    133views Neural Networks» more  ESANN 2004»
    Dimensionality reduction and classification using the distribution mapping exponent
    14 years 7 days ago
    Dimensionality reduction and classification using the distribution mapping exponent
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    : Probability distribution mapping function, which maps multivariate data distribution to the function of one variable, is introduced. Distributionmapping exponent (DME) is somethi...
    Marcel Jirina
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    ESANN
    2004
    109views Neural Networks» more  ESANN 2004»
    On-line support vector machines and optimization strategies
    14 years 7 days ago
    On-line support vector machines and optimization strategies
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    Juan Manuel Górriz, Carlos García Pu...
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    ESANN
    2004
    112views Neural Networks» more  ESANN 2004»
    A Modular Framework for Multi category feature selection in Digital mammography
    14 years 7 days ago
    A Modular Framework for Multi category feature selection in Digital mammography
    Download  www.dice.ucl.ac.be
    Many existing researches utilized many different approaches for recognition in digital mammography using various ANN classifier-modeling techniques. Different types of feature extr...
    Ranadhir Ghosh, Moumita Ghosh, John Yearwood
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    ESANN
    2004
    115views Neural Networks» more  ESANN 2004»
    Non-Euclidean norms and data normalisation
    14 years 7 days ago
    Non-Euclidean norms and data normalisation
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    Kevin Doherty, Rod Adams, Neil Davey
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    ESANN
    2004
    97views Neural Networks» more  ESANN 2004»
    Using Andrews Curves for Clustering and Sub-clustering Self-Organizing Maps
    14 years 7 days ago
    Using Andrews Curves for Clustering and Sub-clustering Self-Organizing Maps
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    Abstract. The use of self-organizing maps to analyze data often depends on finding effective methods to visualize the SOM's structure. In this paper we propose a new way to pe...
    Cesar García-Osorio, Jesús Maudes, C...
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    ESANN
    2004
    90views Neural Networks» more  ESANN 2004»
    Functional radial basis function networks
    14 years 7 days ago
    Functional radial basis function networks
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    There has been recently a lot of interest for functional data analysis [1] and extensions of well-known methods to functional inputs (clustering algorithm [2], non-parametric model...
    Nicolas Delannay, Fabrice Rossi, Brieuc Conan-Guez...
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    ESANN
    2004
    160views Neural Networks» more  ESANN 2004»
    Evolutionary tuning of multiple SVM parameters
    14 years 7 days ago
    Evolutionary tuning of multiple SVM parameters
    Download  www.neuroinformatik.ruhr-uni-bochum.de
    The problem of model selection for support vector machines (SVMs) is considered. We propose an evolutionary approach to determine multiple SVM hyperparameters: The covariance matr...
    Frauke Friedrichs, Christian Igel
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    ESANN
    2004
    90views Neural Networks» more  ESANN 2004»
    High-accuracy value-function approximation with neural networks applied to the acrobot
    14 years 7 days ago
    High-accuracy value-function approximation with neural networks applied to the acrobot
    Download  remi.coulom.free.fr
    Several reinforcement-learning techniques have already been applied to the Acrobot control problem, using linear function approximators to estimate the value function. In this pape...
    Rémi Coulom
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