Pattern classification with mixtures of weighted least-squares support vector machine experts

Lima C.A.M.; Coelho A.L.V.; VonZuben F.J.

Pattern classification with mixtures of weighted least-squares support vector machine experts

Tipo

Artigo

Data de publicação

2009

Periódico

Neural Computing and Applications

Citações (Scopus)

5

Autores

Lima C.A.M.
Coelho A.L.V.
VonZuben F.J.

Resumo

Support Vector Machine (SVM) classifiers are high-performance classification models devised to comply with the structural risk minimization principle and to properly exploit the several SVM variants, Least-Squares SVMs (LS-SVMs) have gained increased attention recently due mainly to their computationally attractive properties coming as the direct result of applying a modified formulation that makes use of a sum-squared-error cost function jointly with equality, instead of inequality, constraints. In this work, we present a flexible hybrid approach aimed at augmenting the proficiency of LS-SVM classifiers with regard to accuracy/generalization as well as to hyperparameter calibration issues. Such approach, named as Mixtures of Weighted Least-Squares Support Vector Machine Experts, centers around the fusion of the weighted variant of LS-SVMs with Mixtures of Experts models. After the formal characterization of the novel learning framework, simulation results obtained with respect to both binary and multiclass pattern classification problems are reported, ratifying the suitability of the novel hybrid approach in improving the performance issues considered. © Springer-Verlag London Limited 2008.