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. IntroductionThe purpose of this paper is twofold. It should serve as a self-contained introduction to support vector regression for readersnew to this rapidly developing field of the otherhand, it attempts to give an overview of recent developments inthe end, we decided to organize the essay as by giving a brief overview of the basic techniques inSections 1, 2 and 3, plus a short summary with a number offigures and diagrams in Section 4.

Statistics and Computing 14: 199–222, 2004 C 2004 Kluwer Academic Publishers. Manufactured in The Netherlands. A tutorial on support vector regression∗ ALEX J. SMOLA and BERNHARD SCHOLKOPF¨ RSISE, Australian National University, Canberra 0200, Australia

Loading..

Information

Related search queries