Dynamically Adapting Kernels in Support Vector Machines Produced as Part of the Esprit Working Group in Neural and Computational Learning Ii, Neurocolt2 27150
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 connrming its validity are presented.