title: Pruning error minimization in least squares support vector machines.
abstract: The support vector machine (SVM) is a method for classification and for function approximation. This method commonly makes use of an /spl epsi/-insensitive cost function, meaning that errors smaller than /spl epsi/ remain unpunished. As an alternative, a least squares support vector machine (LSSVM) uses a quadratic cost function. When the LSSVM method is used for function approximation, a nonsparse solution is obtained. The sparseness is imposed by pruning, i.e., recursively solving the approximation problem and subsequently omitting data that has a small error in the previous pass. However, omitting data with a small approximation error in the previous pass does not reliably predict what the error will be after the sample has been omitted. In this paper, a procedure is introduced that selects from a data set the training sample that will introduce the smallest approximation error when it will be omitted. It is shown that this pruning scheme outperforms the standard one.
authors.affiliation: Drebbel Inst. for Mechatronics, Univ. of Twente, Enschede, Netherlands.
authors.forename: B J
authors.initials: BJ
authors.lastname: de Kruif
authors.forename: T A
authors.initials: TA
authors.lastname: de Vries