InforLorV4, Main, Merge, bibRecord, 00B308

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

Identifieur interne : 00B308 ( Main/Merge ); précédent : 00B307; suivant : 00B309

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

Auteurs : Laurent Bougrain [France] ; Frédéric Alexandre [France]

Source :

RBID : Hal:inria-00107693

Descripteurs français

mix :
- catégorisation, clustering, forecasting, neural networks, prédiction, radio-communication, réseaux de neurones, télécommunication.

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

Url:

https://hal.inria.fr/inria-00107693

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 005085
to stream Hal, to step Curation: 005085
to stream Hal, to step Checkpoint: 006524

Links to Exploration step

Hal:inria-00107693

Le document en format XML

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<front><div type="abstract" xml:lang="en">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</div>
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Unsupervised Connectionist Clustering Algorithms for a better Supervised Prediction : Application to a radio communication problem

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

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