Communication Dans Un Congrès Année : 1999

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

Résumé

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

Fichier principal
Vignette du fichier
99-R-177.pdf (238.34 Ko) Télécharger le fichier

Dates et versions

inria-00107693 , version 1 (19-10-2006)

Licence

Identifiants

  • HAL Id : inria-00107693 , version 1

Citer

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⟩
288 Consultations
435 Téléchargements

Partager

  • More