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    ESANN
    2006
    202views Neural Networks» more  ESANN 2006»
    LS-SVM functional network for time series prediction
    14 years 28 days ago
    LS-SVM functional network for time series prediction
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    Usually time series prediction is done with regularly sampled data. In practice, however, the data available may be irregularly sampled. In this case the conventional prediction me...
    Tuomas Kärnä, Fabrice Rossi, Amaury Lend...
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    ESANN
    2006
    121views Neural Networks» more  ESANN 2006»
    Independent dynamics subspace analysis
    14 years 28 days ago
    Independent dynamics subspace analysis
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    Abstract. The paper presents an algorithm for identifying the independent subspace analysis model based on source dynamics. We propose to separate subspaces by decoupling their dyn...
    Alexander Ilin
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    ESANN
    2006
    117views Neural Networks» more  ESANN 2006»
    Extended model of conditioned learning within latent inhibition
    14 years 28 days ago
    Extended model of conditioned learning within latent inhibition
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    Due to the various and dynamic nature of stimuli, decisions of intelligent agents must rely on the coordination of complex cognitive systems. This paper precisely focusses on a gen...
    Nicolas Gomond, Jean Marc Salotti
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    ESANN
    2006
    130views Neural Networks» more  ESANN 2006»
    Classification by means of Evolutionary Response Surfaces
    14 years 28 days ago
    Classification by means of Evolutionary Response Surfaces
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    Abstract. Response surfaces are a powerful tool for both classification and regression as they are able to model many different phenomena and construct complex boundaries between c...
    Rafael del Castillo Gomariz, Nicolás Garc&i...
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    ESANN
    2006
    108views Neural Networks» more  ESANN 2006»
    Degeneracy in model selection for SVMs with radial Gaussian kernel
    14 years 28 days ago
    Degeneracy in model selection for SVMs with radial Gaussian kernel
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    We consider the model selection problem for support vector machines applied to binary classification. As the data generating process is unknown, we have to rely on heuristics as mo...
    Tobias Glasmachers
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    ESANN
    2006
    110views Neural Networks» more  ESANN 2006»
    Designing neural network committees by combining boosting ensembles
    14 years 28 days ago
    Designing neural network committees by combining boosting ensembles
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    The use of modified Real Adaboost ensembles by applying weighted emphasis on erroneous and critical (near the classification boundary) has been shown to lead to improved designs, ...
    Vanessa Gómez-Verdejo, Aníbal R. Fig...
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    ESANN
    2006
    126views Neural Networks» more  ESANN 2006»
    FPGA implementation of an integrate-and-fire LEGION model for image segmentation
    14 years 28 days ago
    FPGA implementation of an integrate-and-fire LEGION model for image segmentation
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    Abstract. Despite several previous studies, little progress has been made in building successful neural systems for image segmentation in digital hardware. Spiking neural networks ...
    Bernard Girau, Cesar Torres-Huitzil
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    ESANN
    2006
    128views Neural Networks» more  ESANN 2006»
    Visual object classification by sparse convolutional neural networks
    14 years 28 days ago
    Visual object classification by sparse convolutional neural networks
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    Abstract. A convolutional network architecture termed sparse convolutional neural network (SCNN) is proposed and tested on a real-world classification task (car classification). In...
    Alexander Gepperth
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    ESANN
    2006
    103views Neural Networks» more  ESANN 2006»
    Topological Correlation
    14 years 28 days ago
    Topological Correlation
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    Quantifying the success of the topographic preservation achieved with a neural map is difficult. In this paper we present Topological Correlation, Tc, a method that assesses the de...
    Kevin Doherty, Rod Adams, Neil Davey
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    ESANN
    2006
    115views Neural Networks» more  ESANN 2006»
    Learning for stochastic dynamic programming
    14 years 28 days ago
    Learning for stochastic dynamic programming
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    Abstract. We present experimental results about learning function values (i.e. Bellman values) in stochastic dynamic programming (SDP). All results come from openDP (opendp.sourcef...
    Sylvain Gelly, Jérémie Mary, Olivier...
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