Least squares support vector machine classifiers: a large scale algorithm
(1999) Proceedings European Conference Circ.Th. Des. ECCTD′99 — Location: Stresa Italy
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- Abstract
- Support vector machines (SVM's) have been introduced in literature as a method for pattern recognition and function estimation, within the framework of statistical learning theory and structural risk minimization. A least squares version (LSSVM) has been recently reported which expresses the training in terms of solving a set of linear equations instead of quadratic programming as for the standard SVM case. In this paper we present an iterative training algorithm for LS-SVM's which is based on a conjugate gradient method. This enables solving large scale classification problems which is illustrated on a multi two-spiral benchmark problem. Keywords. Support vector machines, classification, neural networks, RBF kernels, conjugate gradient method.
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Suykens, J., Lukas, L., Van Dooren, P., & Vandewalle, J. (1999). Least squares support vector machine classifiers: a large scale algorithm. Proceedings European Conference Circ.Th. Des. ECCTD′99, Stresa Italy. https://hdl.handle.net/2078.5/221962