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Least squares support vector machine classifiers: a large scale algorithm
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1999
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EngineeringMachine LearningBiometricsKernel MethodStandard Svm CaseSupport Vector MachineClassification MethodData ScienceData MiningPattern RecognitionManagementSupport Vector MachinesPredictive AnalyticsKnowledge DiscoveryComputer ScienceData ClassificationClassificationClassifier SystemRbf KernelsLarge Scale AlgorithmLearning Classifier System
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.