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» Interpretation of Trained Neural Networks by Rule Extraction
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NN
2002
Springer
125views Neural Networks» more  NN 2002»
13 years 10 months ago
Generalized relevance learning vector quantization
We propose a new scheme for enlarging generalized learning vector quantization (GLVQ) with weighting factors for the input dimensions. The factors allow an appropriate scaling of ...
Barbara Hammer, Thomas Villmann
JMLR
2012
12 years 1 months ago
Deep Boltzmann Machines as Feed-Forward Hierarchies
The deep Boltzmann machine is a powerful model that extracts the hierarchical structure of observed data. While inference is typically slow due to its undirected nature, we argue ...
Grégoire Montavon, Mikio L. Braun, Klaus-Ro...
ML
1998
ACM
153views Machine Learning» more  ML 1998»
13 years 10 months ago
Bayesian Landmark Learning for Mobile Robot Localization
To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optim...
Sebastian Thrun
ESANN
2003
14 years 5 days ago
Semi-automatic acquisition and labelling of image data using SOMs
Abstract. Application of neural networks for real world object recognition suffers from the need to acquire large quantities of labelled image data. We propose a solution that acq...
Gunther Heidemann, Axel Saalbach, Helge Ritter
CBMS
1997
IEEE
14 years 3 months ago
Radial basis function-based image segmentation using a receptive field
This paper presents a novel method for CT head image automatic segmentation. The images are obtained from patients having the spontaneous intra cerebral brain hemorrhage ICH. Th...
Domagoj Kovacevic, Sven Loncaric