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    ESANN
    2003
    66views Neural Networks» more  ESANN 2003»
    Meter value recognition using locally connected hierarchical networks
    14 years 28 days ago
    Meter value recognition using locally connected hierarchical networks
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    This paper describes a two-stage system for the recognition ge meter values. A feed-forward Neural Abstraction Pyramid is initialized in an unsupervised manner and trained in a sup...
    Sven Behnke
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    ESANN
    2003
    142views Neural Networks» more  ESANN 2003»
    Comparison of neural algorithms for blind source separation in sensor array applications
    14 years 28 days ago
    Comparison of neural algorithms for blind source separation in sensor array applications
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    - A test bed of experiments with real and artificially generated data has been designed to compare the performance of three well-known algorithms for BSS. The main goal of these ex...
    Guillermo Bedoya, Sergio Bermejo, Joan Cabestany
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    ESANN
    2003
    112views Neural Networks» more  ESANN 2003»
    VLSI Realization of a Two-Dimensional Hamming Distance Comparator ANN for Image Processing Applications
    14 years 28 days ago
    VLSI Realization of a Two-Dimensional Hamming Distance Comparator ANN for Image Processing Applications
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    This paper presents the hardware realization of a Hamming artificial neural network, and demonstrates its use in a high-speed precision alignment system. High degree of parallelism...
    Stéphane Badel, Alexandre Schmid, Yusuf Leb...
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    ESANN
    2003
    65views Neural Networks» more  ESANN 2003»
    Robust Topology Representing Networks
    14 years 28 days ago
    Robust Topology Representing Networks
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    Martinetz and Schulten proposed the use of a Competitive Hebbian Learning (CHL) rule to build Topology Representing Networks. From a set of units and a data distribution, a link i...
    Michaël Aupetit
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    ESANN
    2003
    82views Neural Networks» more  ESANN 2003»
    High-dimensional labeled data analysis with Gabriel graphs
    14 years 28 days ago
    High-dimensional labeled data analysis with Gabriel graphs
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    We propose the use of the Gabriel graph for the exploratory analysis of potentially high dimensional labeled data. Gabriel graph is a subgraph of the Delaunay triangulation, which ...
    Michaël Aupetit
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    ESANN
    2003
    92views Neural Networks» more  ESANN 2003»
    On Convergence Problems of the EM Algorithm for Finite Gaussian Mixtures
    14 years 28 days ago
    On Convergence Problems of the EM Algorithm for Finite Gaussian Mixtures
    Download  www.dice.ucl.ac.be
    Efficient probability density function estimation is of primary interest in statistics. A popular approach for achieving this is the use of finite Gaussian mixture models. Based on...
    Cédric Archambeau, John Aldo Lee, Michel Ve...
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    ESANN
    2003
    98views Neural Networks» more  ESANN 2003»
    A Fuzzy ARTMAP Probability Estimator with Relevance Factor
    14 years 28 days ago
    A Fuzzy ARTMAP Probability Estimator with Relevance Factor
    Download  www.dice.ucl.ac.be
    Abstract. An incremental, nonparametric probability estimation procedure using a variation of the Fuzzy ARTMAP (FAM) neural network is introduced. The resulted network, called Fuzz...
    Razvan Andonie, Lucian Sasu
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    ESANN
    2003
    85views Neural Networks» more  ESANN 2003»
    The hypersphere neuron
    14 years 28 days ago
    The hypersphere neuron
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    In this paper a special higher order neuron, the hypersphere neuron, is introduced. By embedding Euclidean space in a conformal space, hyperspheres can be expressed as vectors. The...
    Vladimir Banarer, Christian Perwass, Gerald Sommer
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    ESANN
    2003
    136views Neural Networks» more  ESANN 2003»
    Neural Networks and M5 model trees in modeling water level-discharge relationship for an Indian river
    14 years 28 days ago
    Neural Networks and M5 model trees in modeling water level-discharge relationship for an Indian river
    Download  www.dice.ucl.ac.be
    : In flood management it is important to reliably estimate the discharge in a river. Hydrologists use historic data to establish a rating curve – a relationship between the water...
    Biswanath Bhattacharya, Dimitri P. Solomatine
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    ESANN
    2001
    162views Neural Networks» more  ESANN 2001»
    A divide-and-conquer learning architecture for predicting unknown motion
    14 years 28 days ago
    A divide-and-conquer learning architecture for predicting unknown motion
    Download  www.dice.ucl.ac.be
    Time varying environments or model selection problems lead to crucial dilemmas in identification and control science. In this paper, we propose a modular prediction scheme consisti...
    Patrice Wira, Jean-Philippe Urban, Julien Gresser
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