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    ESANN
    2008
    143views Neural Networks» more  ESANN 2008»
    Computational model for amygdala neural networks
    14 years 1 months ago
    Computational model for amygdala neural networks
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    We present a computational model of amygdala neural networks. It is used to simulate neuronal activation in amygdala nuclei at different stages of aversive conditioning experiments...
    Jean Marc Salotti
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    ESANN
    2008
    131views Neural Networks» more  ESANN 2008»
    Parallelizing single patch pass clustering
    14 years 1 months ago
    Parallelizing single patch pass clustering
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    Clustering algorithms such as k-means, the self-organizing map (SOM), or Neural Gas (NG) constitute popular tools for automated information analysis. Since data sets are becoming l...
    Nikolai Alex, Barbara Hammer
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    ESANN
    2008
    112views Neural Networks» more  ESANN 2008»
    Homogeneous bipartition based on multidimensional ranking
    14 years 1 months ago
    Homogeneous bipartition based on multidimensional ranking
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    We present an algorithm which partitions a data set in two parts with equal size and experimentally nearly the same distribution measured through the likelihood of a Parzen kernel ...
    Michaël Aupetit
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    ESANN
    2008
    210views Neural Networks» more  ESANN 2008»
    Petri nets design based on neural networks
    14 years 1 months ago
    Petri nets design based on neural networks
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    Petri net faulty models are useful for reliability analysis and fault diagnosis of discrete event systems. Such models are difficult to work out as long as they must be computed ac...
    Edouard Leclercq, Souleiman Ould el Medhi, Dimitri...
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    ESANN
    2008
    127views Neural Networks» more  ESANN 2008»
    Using graph-theoretic measures to predict the performance of associative memory models
    14 years 1 months ago
    Using graph-theoretic measures to predict the performance of associative memory models
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    We test a selection of associative memory models built with different connection strategies, exploring the relationship between the structural properties of each network and its pa...
    Lee Calcraft, Rod Adams, Weiliang Chen, Neil Davey
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    ESANN
    2008
    116views Neural Networks» more  ESANN 2008»
    Rank-based quality assessment of nonlinear dimensionality reduction
    14 years 1 months ago
    Rank-based quality assessment of nonlinear dimensionality reduction
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    Abstract. Nonlinear dimensionality reduction aims at providing lowdimensional representions of high-dimensional data sets. Many new methods have been proposed in the recent years, ...
    John Aldo Lee, Michel Verleysen
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    ESANN
    2008
    188views Neural Networks» more  ESANN 2008»
    An emphasized target smoothing procedure to improve MLP classifiers performance
    14 years 1 months ago
    An emphasized target smoothing procedure to improve MLP classifiers performance
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    Standard learning procedures are better fitted to estimation than to classification problems, and focusing the training on appropriate samples provides performance advantages in cl...
    Soufiane El Jelali, Abdelouahid Lyhyaoui, An&iacut...
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    ESANN
    2008
    107views Neural Networks» more  ESANN 2008»
    The gamma cycle and its role in the formation of assemblies
    14 years 1 months ago
    The gamma cycle and its role in the formation of assemblies
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    Rhythmic synchronization of activated neural groups in the gamma-frequency range (30-100 Hz) is observed in many brain regions. Interneuron networks are key to the generation of th...
    Thomas Burwick
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    ESANN
    2008
    107views Neural Networks» more  ESANN 2008»
    On related violating pairs for working set selection in SMO algorithms
    14 years 1 months ago
    On related violating pairs for working set selection in SMO algorithms
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    Sequential Minimal Optimization (SMO) is currently the most popular algorithm to solve large quadratic programs for Support Vector Machine (SVM) training. For many variants of this...
    Tobias Glasmachers
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    ESANN
    2008
    117views Neural Networks» more  ESANN 2008»
    Computationally Efficient Neural Field Dynamics
    14 years 1 months ago
    Computationally Efficient Neural Field Dynamics
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    We propose a modification of the dynamic neural field model of Amari [1], aiming at reducing the simulation effort by employing spaceand frequency representations of the dynamic st...
    Alexander Gepperth, Jannik Fritsch, Christian Goer...
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