PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Tutorial on Support Vector Regression
Alex Smola and Bernhard Schoelkopf
Statistics and Computing Volume 14, pp. 199-222, 2004. ISSN 0960-3174

Abstract

In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:856
Deposited By:Adam Kowalczyk
Deposited On:02 January 2005