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ESANN
2008
9 years 4 months ago
Simulation of a recurrent neurointerface with sparse electrical connections
With the technical development of multi-electrode arrays, the monitoring of many individual neurons has become feasible. However, for practical use of those arrays as bidirectional...
Andreas Herzog, Karsten Kube, Bernd Michaelis, Ana...
ESANN
2008
9 years 4 months ago
K-nearest neighbours based on mutual information for incomplete data classification
Incomplete data is a common drawback that machine learning techniques need to deal with when solving real-life classification tasks. One of the most popular procedures for solving ...
Pedro J. García-Laencina, José-Luis ...
ESANN
2008
9 years 4 months ago
An accelerated MDM algorithm for SVM training
In this work we will propose an acceleration procedure for the Mitchell
Álvaro Barbero Jiménez, Jorge L&oacu...
ESANN
2008
9 years 4 months ago
Neural networks for computational neuroscience
Computational neuroscience is an appealing interdisciplinary domain, at the interface between biology and computer science. It aims at understanding the experimental data obtained...
David Meunier, Hélène Paugam-Moisy
ESANN
2008
9 years 4 months ago
Methodology and standards for data analysis with machine learning tools
Many tools for data mining are complex and require skills and experience to be used successfully. Therefore, data mining is often considered an art as much as science. This paper p...
Damien François
ESANN
2008
9 years 4 months ago
Generalized matrix learning vector quantizer for the analysis of spectral data
The analysis of spectral data constitutes new challenges for machine learning algorithms due to the functional nature of the data. Special attention is paid to the metric used in t...
Petra Schneider, Frank-Michael Schleif, Thomas Vil...
ESANN
2008
9 years 4 months ago
Metric adaptation for supervised attribute rating
A new approach for faithful relevance rating of attributes is proposed, enabling class-specific discriminatory data space transformations. The method is based on the adaptation of ...
Marc Strickert, Frank-Michael Schleif, Thomas Vill...
ESANN
2008
9 years 4 months ago
Approximation of Gaussian process regression models after training
The evaluation of a standard Gaussian process regression model takes time linear in the number of training data points. In this paper, the models are approximated in the feature sp...
Thorsten Suttorp, Christian Igel
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