An Adaptive Condensing Algorithm based on Mixtures of Gaussians

Lozano, M. ; Sotoca, JM. ; Sánchez, JS. ; Pla, F.

Resumen: In this paper, a new adaptive approach to training set size reduction, estimating probability density functions is presented. This scheme consists of defining a very small number of prototypes that represent all the original instances, using mixtures of gaussians. Although the ultimate aim of the algorithm proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over eleven real datasets by comparing not only the reduction rate but also the classification accuracy

Idioma: Inglés

Año: 2004

Publicado en: Frontiers in Artificial Intelligence and Applications 113 (2004), 225-232
ISSN: 0922-6389

Tipo y forma: Artículo (PostPrint)