On-line support vector machines and optimization strategies

In this work we derive a new on-line parametric model for time series forecasting based on Vapnik-Chervonenkis (VC) theory. Using the strong connection between support vector machines (SVM) and Regularization theory (RT), we propose a regularization operator in order to obtain a suitable expansion of radial basis functions (RBFs) with the corresponding expressions for updating neural parameters. This operator seeks for the “flattest” function in a feature space, minimizing the risk functional. Finally we mention some modifications and extensions that can be applied to control neural resources and select relevant input space in order to avoid high computational effort (batch learning).