2008
DOI: 10.1007/s00521-008-0210-6
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Pattern classification with mixtures of weighted least-squares support vector machine experts

Abstract: Support Vector Machine (SVM) classifiers are high-performance classification models devised to comply with the structural risk minimization principle and to properly exploit the kernel artifice of nonlinearly mapping input data into high-dimensional feature spaces toward the automatic construction of better discriminating linear decision boundaries. Among several SVM variants, LeastSquares SVMs (LS-SVMs) have gained increased attention recently due mainly to their computationally attractive properties coming a… Show more

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Cited by 5 publications
(1 citation statement)
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References 29 publications
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“…This allows us to explore the standard FFNN and Linear Regression Experts, as well as LS-SVM Experts. In addition, HME with LS-SVM is a more general implementation of the Mixture of Experts with LS-SVM presented in [23]. That work integrated LS-SVM experts using a single Gating network, while we are able to support a hierarchy of mixtures.…”
Section: Hierarchical Mixture Of Expertsmentioning
confidence: 99%
“…This allows us to explore the standard FFNN and Linear Regression Experts, as well as LS-SVM Experts. In addition, HME with LS-SVM is a more general implementation of the Mixture of Experts with LS-SVM presented in [23]. That work integrated LS-SVM experts using a single Gating network, while we are able to support a hierarchy of mixtures.…”
Section: Hierarchical Mixture Of Expertsmentioning
confidence: 99%