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Titre
Maximum Likelihood Topology Preserving Ensembles
Autor(es)
Sujet
Computer Science
Fecha de publicación
2006
Éditeur
Springer Science + Business Media
Citación
Intelligent Data Engineering and Automated Learning – IDEAL 2006 Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 4224, pp. 1434-1442.
Resumen
Statistical re-sampling techniques have been used extensively and successfully in the machine learning approaches for generations of classifier and predictor ensembles. It has been frequently shown that combining so called unstable predictors has a stabilizing effect on and improves the performance of the prediction system generated in this way. In this paper we use the re-sampling techniques in the context of a topology preserving map which can be used for scale invariant classification, taking into account the fact that it models the residual after feedback with a family of distributions and finds filters which make the residuals most likely under this model. This model is applied to artificial data sets and compared with a similar version based on the Self Organising Map (SOM).
URI
ISBN
978-3-540-45485-4 (Print) / 978-3-540-45487-8 (Online)
ISSN
0302-9743 (Print) / 1611-3349 (Online)
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