Hybrid SOM-SVM Algorithm for Real Time Series Forecasting
نویسندگان
چکیده
In this paper we show a new on-line parametric model for time series forecasting based on VapnikChervonenkis (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.
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تاریخ انتشار 2004