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    ESANN
    2006
    192views Neural Networks» more  ESANN 2006»
    Margin based Active Learning for LVQ Networks
    14 years 1 months ago
    Margin based Active Learning for LVQ Networks
    Download  www2.in.tu-clausthal.de
    In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation...
    Frank-Michael Schleif, Barbara Hammer, Thomas Vill...
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    ESANN
    2006
    129views Neural Networks» more  ESANN 2006»
    Iterative context compilation for visual object recognition
    14 years 1 months ago
    Iterative context compilation for visual object recognition
    Download  www.dice.ucl.ac.be
    This contribution describes an almost parameterless iterative context compilation method, which produces feature layers, that are especially suited for mixed bottom-up top-down ass...
    Jens Teichert, Rainer Malaka
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    ESANN
    2006
    202views Neural Networks» more  ESANN 2006»
    Data topology visualization for the Self-Organizing Map
    14 years 1 months ago
    Data topology visualization for the Self-Organizing Map
    Download  www.dice.ucl.ac.be
    The Self-Organizing map (SOM), a powerful method for data mining and cluster extraction, is very useful for processing data of high dimensionality and complexity. Visualization met...
    Kadim Tasdemir, Erzsébet Merényi
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    ESANN
    2006
    116views Neural Networks» more  ESANN 2006»
    Nonlinear dynamics in neural computation
    14 years 1 months ago
    Nonlinear dynamics in neural computation
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    This tutorial reports on the use of nonlinear dynamics in several different models of neural systems. We discuss a number of distinct approaches to neural information processing ba...
    Tjeerd Olde Scheper, Nigel Crook
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    ESANN
    2006
    182views Neural Networks» more  ESANN 2006»
    Adaptive Sensor Modelling and Classification using a Continuous Restricted Boltzmann Machine (CRBM)
    14 years 1 months ago
    Adaptive Sensor Modelling and Classification using a Continuous Restricted Boltzmann Machine (CRBM)
    Download  www.see.ed.ac.uk
    A probabilistic, ``neural'' approach to sensor modelling and classification is described, performing local data fusion in a wireless system for embedded sensors using a ...
    Tong Boon Tang, Alan F. Murray
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    ESANN
    2006
    107views Neural Networks» more  ESANN 2006»
    Time series prediction using DirRec strategy
    14 years 1 months ago
    Time series prediction using DirRec strategy
    Download  eprints.pascal-network.org
    This paper demonstrates how the selection of Prediction Strategy is important in the Long-Term Prediction of Time Series. Two strategies are already used in the prediction purposes...
    Antti Sorjamaa, Amaury Lendasse
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    ESANN
    2006
    85views Neural Networks» more  ESANN 2006»
    Synthesis of maximum margin and multiview learning using unlabeled data
    14 years 1 months ago
    Synthesis of maximum margin and multiview learning using unlabeled data
    Download  www.dice.ucl.ac.be
    In this presentation we show the semi-supervised learning with two input sources can be transformed into a maximum margin problem to be similar to a binary SVM. Our formulation exp...
    Sándor Szedmák, John Shawe-Taylor
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    ESANN
    2006
    94views Neural Networks» more  ESANN 2006»
    Consistent estimation of the architecture of multilayer perceptrons
    14 years 1 months ago
    Consistent estimation of the architecture of multilayer perceptrons
    Download  www.dice.ucl.ac.be
    Abstract. We consider regression models involving multilayer perceptrons (MLP) with one hidden layer and a Gaussian noise. The estimation of the parameters of the MLP can be done b...
    Joseph Rynkiewicz
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    ESANN
    2006
    111views Neural Networks» more  ESANN 2006»
    Efficient Forward Regression with Marginal Likelihood
    14 years 1 months ago
    Efficient Forward Regression with Marginal Likelihood
    Download  www.dice.ucl.ac.be
    We propose an efficient forward regression algorithm based on greedy optimization of marginal likelihood. It can be understood as a forward selection procedure which adds a new bas...
    Ping Sun, Xin Yao
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    ESANN
    2006
    106views Neural Networks» more  ESANN 2006»
    Using sampling methods to improve binding site predictions
    14 years 1 months ago
    Using sampling methods to improve binding site predictions
    Download  www.dice.ucl.ac.be
    Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we combine random selection under-sampling with th...
    Yi Sun, Mark Robinson, Rod Adams, Rene te Boekhors...
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