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NeuroCOLT
Technical Report NC-TR-98-030
A
Tutorial on Support Vector Regression
Alex J. Smola
GMD
Bernhard Schoelkopf
GMD
Received:
30-OCT-98
Abstract
In this tutorial we give an
overview of the basic ideas underlying Support Vector (SV) machines
for regression and 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 and capacity control from
a SV point of view.
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