Paper Info
Title
Unsupervised connectionist clustering algorithms for a better supervised prediction: application to a radio communication problem
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 1988, neural gas, growing neural gas, Buhmann's algorithm 1992) to obtain classes homogeneous enough to decrease the predictive error of the radio electrical wave prediction
Year
DOI
Venue
1999
10.1109/IJCNN.1999.836220
IJCNN
Keywords
Field
DocType
mobile radio,multilayer perceptrons,pattern clustering,telecommunication computing,kohonen maps,contextual subsets,geographic database,growing neural gas,mobile communication,multilayer perceptron,radio communication problem,radio electrical wave dying down prediction,supervised prediction,unsupervised algorithms,unsupervised connectionist clustering algorithms,databases,prediction,forecasting,clustering,neural gas,prototypes,prediction error,data mining,clustering algorithms,structured data,testing,predictive models,prediction model,telecommunication,neural networks,attenuation
Data mining,Computer science,Self-organizing map,Multilayer perceptron,Artificial intelligence,Cluster analysis,Artificial neural network,Connectionism,Mobile radio,Pattern recognition,Subspace topology,Neural gas,Machine learning
Conference
Volume
ISSN
ISBN
5
1098-7576
0-7803-5529-6
Citations 
PageRank 
References 
2
0.36
4
Authors
2
Name
Order
Citations
PageRank
Bougrain, L.120.36
Frédéric Alexandre272.43