Online Support Vector Regression
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TL;DR
An online version of the algorithm for training the support vector machine for regression and also how it has been extended in order to be more flexible for the hyper parameter estimation is presented.
Abstract
Many approaches for obtaining systems with intelligent behavior are based on components that learn automatically from previous experience. The development of these learning techniques is the objective of the area of research known as machine learning. During the last decade, researchers have produced numerous and outstanding advances in this area, boosted by the successful application of machine learning techniques. This thesis presents one of this techniques, an online version of the algorithm for training the support vector machine for regression and also how it has been extended in order to be more flexible for the hyper parameter estimation. Furthermore the algorithm has been compared with a batch implementation and tested in a real application at the Department of Information, Systematics and Telematics in Genoa.