Authors:
J. M. Górriz
1
;
C. G. Puntonet
2
and
E. W. Lang
3
Affiliations:
1
University of Cádiz, Spain
;
2
University of Granada, Spain
;
3
University of Regensburg, Germany
Keyword(s):
Support vector machines, structural risk minimization, kernel, on-line algorithms, matrix decompositions,
resource allocating network.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Enterprise Information Systems
;
Hybrid Learning Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge-Based Systems Applications
;
Machine Learning in Control Applications
Abstract:
In this paper we show 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 attest 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.