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Unsupervised Connectionist Clustering Algorithms for a better Supervised Prediction : Application to a radio communication problem

Laurent Bougrain 1 Frédéric Alexandre 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Most models concerned with real-world applications can be improved in structuring data and incorporating knowledge about the domain. In our problem of radio electrical wave dying down prediction for mobile communication, a geographic database can be divided in contextual subsets, each representing an homogeneous domain where a predictive model performs better. More precisely, by clustering the input space, a predictive model (here a multilayer perceptron) can be trained on each subspace. Various unsupervised algorithms for clustering were evaluated (Kohonen's maps. Desieno's algorithm, Neural gas, Growing Neural Gas, Buhmann's algorithm) to obtain class homogeneous enough to decrease the predictive error of the radio electrical wave prediction
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https://hal.inria.fr/inria-00107693
Contributor : Publications Loria <>
Submitted on : Thursday, October 19, 2006 - 9:05:27 AM
Last modification on : Friday, February 26, 2021 - 3:28:03 PM
Long-term archiving on: : Wednesday, March 29, 2017 - 12:54:53 PM

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  • HAL Id : inria-00107693, version 1

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Laurent Bougrain, Frédéric Alexandre. Unsupervised Connectionist Clustering Algorithms for a better Supervised Prediction : Application to a radio communication problem. International Joint Conference on Neural Networks, International Neural Networks Society, 1999, Washington, USA, 6 p. ⟨inria-00107693⟩

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