Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/43082
Título: A boosting method with gaussian mixtures as base learners in a low-dimension space
Autores/as: Martín-González, S. I. 
Lorenzo-García, F. D.
Navarro-Mesa, J. L. 
Ravelo-García, A. G. 
Quintana-Morales, P. J. 
Hernández-Pérez, E. 
Clasificación UNESCO: 3307 Tecnología electrónica
Fecha de publicación: 2007
Publicación seriada: Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
Conferencia: 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006 
Resumen: In this paper we propose a classification method in the context of Boosting called Transformed Space Boosting (TSB). Our aim is to develop the idea of using a combination of Gaussian Mixture Models and transformation matrices to design 'non-weak' base learners in Boosting strategies. The use of transformation matrices makes it possible to do a linear dimensionality reduction from an original space to a transformed one. This leads to a two-steps method where in the first one a single-component mixture is trained in the original space. In the second step, based on the single Gaussian previously trained, we apply the concept of average divergence measure to estimate the transformation matrix. The final classifier achieves an improvement in performance compared to other methods also based on dimensionality reduction. This is clearly seen from the experiments we present which strength the validity of our method and show promising classification scores. © 2006 IEEE.
URI: http://hdl.handle.net/10553/43082
ISBN: 1424406560
9781424406562
DOI: 10.1109/MLSP.2006.275548
Colección:Actas de congresos
Vista completa

Visitas

108
actualizado el 06-jul-2024

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.