2024-03-29T11:21:13Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1350722022-02-07T15:36:22Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134811
2017-09-06T09:16:17Z
urn:hdl:10366/135072
Maximum Likelihood Topology Preserving Ensembles
Corchado Rodríguez, Emilio Santiago
Baruque, Bruno
Gabrys, Bogdan
Computer Science
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).
2017-09-06T09:16:17Z
2017-09-06T09:16:17Z
2006
info:eu-repo/semantics/article
Intelligent Data Engineering and Automated Learning – IDEAL 2006 Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 4224, pp. 1434-1442.
978-3-540-45485-4 (Print) / 978-3-540-45487-8 (Online)
0302-9743 (Print) / 1611-3349 (Online)
http://hdl.handle.net/10366/135072
en
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Unported
Springer Science + Business Media