Abstract
In this paper we prove the analytic connection between Support Vector Machines (SVM) and Regularization Theory (RT) and show, based on this prove, a new on-line parametric model for time series forecasting based on Vapnik-Chervonenkis (VC) theory. Using the latter strong connection, we propose a regularization operator in order to obtain a suitable expansion of radial basis functions (RBFs) and 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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Keywords
- Singular Value Decomposition
- Online Algorithm
- Regularization Theory
- Time Series Forecast
- Time Series Prediction
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Górriz, J.M., Puntonet, C.G., Salmerón, M. (2004). Online Algorithm for Time Series Prediction Based on Support Vector Machine Philosophy. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3037. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24687-9_7
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DOI: https://doi.org/10.1007/978-3-540-24687-9_7
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