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NeuroCOLT
Technical Report NC-TR-98-031
New
Support Vector Algorithms
Bernhard Schölkopf
GMD
Alex J. Smola
GMD
Robert Williamson
Australian National University
Peter Bartlett
Australian National University
Received:
26-NOV-98
Abstract
We describe a new
class of Support Vector algorithms for regression and classification.
In these algorithms, a parameter * lets one effectively control the
number of Support Vectors. While this can be useful in its own right,
the parametrization has the additional benefit of enabling us to eliminate
one of the other free parameters of the algorithm: the accuracy parameter
" in the regression case, and the regularization constant C in
the classification case. We describe the algorithms, give some
theoretical results concerning the meaning and the choice of *, and
report experimental results.
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