Dynamic Modeling by Support Vector Machines

Kernel methods are very popular for static modeling, i.e. classification or nonlinear regression. Only few attempts have been made at extending the concepts to dynamic modeling, i.e. models where some or all variables of the model are the predictions provided by the model at previous sampling times. The feedback thus introduced results in an increased complexity; it is well known, however, that, in the presence of output noise, the optimal model is a recurrent model. Therefore, for modeling processes with output noise, which is a very frequent situation, taking feedback into account during training is mandatory.