Dynamically Adapting Kernels in Support Vector Machines

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Nello Cristianini, Colin Campbell, John Shawe-Taylor


The kernel-parameter is one of the few tunable parameters in Sup(cid:173) port Vector machines, controlling the complexity of the resulting hypothesis. Its choice amounts to model selection and its value is usually found by means of a validation set. We present an algo(cid:173) rithm which can automatically perform model selection with little additional computational cost and with no need of a validation set . In this procedure model selection and learning are not separate, but kernels are dynamically adjusted during the learning process to find the kernel parameter which provides the best possible upper bound on the generalisation error. Theoretical results motivating the approach and experimental results confirming its validity are presented.