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    ESANN
    2007
    114views Neural Networks» more  ESANN 2007»
    Tracking fast changing non-stationary distributions with a topologically adaptive neural network: application to video tracking
    13 years 6 months ago
    Tracking fast changing non-stationary distributions with a topologically adaptive neural network: application to video tracking
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    In this paper, an original method named GNG-T, extended from GNG-U algorithm by [1] is presented. The method performs continuously vector quantization over a distribution that chan...
    Georges Adrian Drumea, Hervé Frezza-Buet
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    ESANN
    2007
    115views Neural Networks» more  ESANN 2007»
    Ensemble neural classifier design for face recognition
    13 years 6 months ago
    Ensemble neural classifier design for face recognition
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    A method for tuning MLP learning parameters in an ensemble classifier framework is presented. No validation set or cross-validation technique is required to optimize parameters for...
    Terry Windeatt
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    ESANN
    2007
    110views Neural Networks» more  ESANN 2007»
    Functional elements and networks in fMRI
    13 years 6 months ago
    Functional elements and networks in fMRI
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    Abstract. We propose a two-step approach for the analysis of functional magnetic resonance images, in the context of natural stimuli. In the first step, elements of functional bra...
    Jarkko Ylipaavalniemi, Eerika Savia, Ricardo Vig&a...
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    ESANN
    2007
    135views Neural Networks» more  ESANN 2007»
    Kernel PCA based clustering for inducing features in text categorization
    13 years 6 months ago
    Kernel PCA based clustering for inducing features in text categorization
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    We study dimensionality reduction or feature selection in text document categorization problem. We focus on the first step in building text categorization systems, that is the cho...
    Zsolt Minier, Lehel Csató
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    ESANN
    2007
    124views Neural Networks» more  ESANN 2007»
    Reinforcement learning in a nutshell
    13 years 6 months ago
    Reinforcement learning in a nutshell
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    Verena Heidrich-Meisner, Martin Lauer, Christian I...
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    ESANN
    2007
    96views Neural Networks» more  ESANN 2007»
    SOM+EOF for finding missing values
    13 years 6 months ago
    SOM+EOF for finding missing values
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    In this paper, a new method for the determination of missing values in temporal databases is presented. This new method is based on two projection methods: a nonlinear one (Self-Or...
    Antti Sorjamaa, Paul Merlin, Bertrand Maillet, Ama...
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    ESANN
    2007
    122views Neural Networks» more  ESANN 2007»
    The Recurrent Control Neural Network
    13 years 6 months ago
    The Recurrent Control Neural Network
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    This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in di...
    Anton Maximilian Schäfer, Steffen Udluft, Han...
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    ESANN
    2007
    127views Neural Networks» more  ESANN 2007»
    Markovian blind separation of non-stationary temporally correlated sources
    13 years 6 months ago
    Markovian blind separation of non-stationary temporally correlated sources
    Download  www.dice.ucl.ac.be
    In a previous work, we developed a quasi-efficient maximum likelihood approach for blindly separating stationary, temporally correlated sources modeled by Markov processes. In this...
    Rima Guidara, Shahram Hosseini, Yannick Deville
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    ESANN
    2007
    96views Neural Networks» more  ESANN 2007»
    A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning
    13 years 6 months ago
    A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning
    Download  www.dice.ucl.ac.be
    Abstract. Canonical Correlation Analysis(CCA) is a useful tool to discover relationship between different sources of information represented by vectors. The solution of the underl...
    Sándor Szedmák, Tijl De Bie, David R...
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    ESANN
    2007
    103views Neural Networks» more  ESANN 2007»
    Interval discriminant analysis using support vector machines
    13 years 6 months ago
    Interval discriminant analysis using support vector machines
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    Imprecision, incompleteness, prior knowledge or improved learning speed can motivate interval–represented data. Most approaches for SVM learning of interval data use local kernel...
    Cecilio Angulo, Davide Anguita, Luis Gonzál...
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