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This paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of the method of Lagrange multipliers. Recently,...
This Letter presents a new approach to time-series modelling using the support vector machines (SVM). Although the γ-filter can provide stability in several time-series models, the SVM is proposed here to provide robustness in the estimation of the γ-filter coefficients. Examples in chaotic time-series prediction and channel equalization show the advantages of the joint SVM γ-filter.
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