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NeuroCOLT
Technical Report NC-TR-98-017
Dynamically
Adapting Kernels in Support Vector Machines
Nello Cristianini
& Colin Campbell
Dept. of Engineering Mathematics
Bristol
John Shawe-Taylor
Dept. of Computer Science
RHUL
Keywords:
Support Vector Machine, Kernel-Adatron,
Statistical Mechanics, Adaptive Kernels, Model Order Selection
Received:
31-MAY-98
Abstract
The kernel-parameter
is one of the few tunable parameters in Support Vector machines, and
controls the complexity of the resulting hypothesis. The choice of
its value amounts to model selection, and is usually performed by
means of a validation set. We present an algorithm which can automatically
perform model selection and learning with no additional computational
cost and with no need of a validation set. Theoretical results
motivating this approach providing upper bounds on the generalisation
error and experimental results confirming its validity are presented.
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