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A tutorial on support vector regression

Smola, Alexander; Schoelkopf, Bernhard


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.

CollectionsANU Research Publications
Date published: 2004
Type: Journal article
Source: Statistics and Computing
DOI: 10.1023/B:STCO.0000035301.49549.88


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