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In this paper, a toolbox LS-SVMlab for Matlab with implementations for a number of LS-SVM related algorithms is presented. The core of the toolbox is a performant LS-SVM training and simulation environment written in C-code. The functionality for classification, function approximation and unsuperpervised learning problems as well time-series prediction is(More)
In this paper we investigate the use of compactly supported RBF kernels for nonlinear function estimation with LS-SVMs. The choice of compact kernels recently proposed by Genton may lead to computational improvements and memory reduction. Examples however illustrate that compactly supported RBF kernels may lead to severe loss in generalization performance(More)
In this paper we propose a new method for learning a combination of estimators. Classically, committee networks are constructed after training the networks independently from each other. Here we present a learning strategy where the training is done in a coupled way. We illustrate that combining parameterized kernel methods with output coupling and use of a(More)
In this paper we propose the concept of coupling for ensemble learning. In the existing literature, all submodels that are considered within an ensemble are trained independently from each other. Here we study the effect of coupling the individual training processes within an ensemble of regularization networks. The considered coupling set gives the(More)
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