The Intrinsic Recurrent Support Vector Machine

In this work, we present a new model for a Recurrent Support Vector Machine. We call it intrinsic because the complete recurrence is directly incorporated within the considered optimisation problem. This approach offers the advantage that the model straightforwardly develops an algorithmic solution. We test the algorithm on several simple time series. The results are promising and can be seen as a starting point for further research. By inventing better and more efficient methods and algorithms, we expect that Recurrent Support Vector Machines could become an alternative to handle and simulate dynamical systems.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Daniel Schneegaß, Anton Maximilian Schäfer, Thomas Martinetz
• Keywords: ESANN 2007, Neural Networks, Recurrent Support Vector, Simple Time Series, Support Vector Machines