Transcription of A tutorial on support vector regression - Smola
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A tutorial on support vector regression - Smola
Statistics and Computing14:199 222, 2004C 2004 Kluwer Academic in The on support vector regression ALEX J. Smola and BERNHARD SCH OLKOPFRSISE, Australian National University, Canberra 0200, f ur biologische Kybernetik, 72076 T ubingen, July 2002 and accepted November 2003In this tutorial we give an overview of the basic ideas underlying support vector (SV) machines forfunction estimation. Furthermore, we include a summary of currently used algorithms for trainingSV machines, covering both the quadratic (or convex) programming part and advanced methods fordealing with large datasets. Finally, we mention some modifications and extensions that have beenapplied to the standard SV algorithm, and discuss the aspect of regularization from a SV :machine learning, support vector machines, regression estimation1.
1. Introduction The purpose of this paper is twofold. It should serve as a self-contained introduction to Support Vector regression for readers new to this rapidly developing field of research.1 On the other hand, it attempts to give an overview of recent developments in the field. To this end, we decided to organize the essay as follows.
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