DocumentCode
703589
Title
Adaptive nonlinear filtering with the support vector method
Author
Mattera, Davide ; Palmieri, Francesco ; Haykin, Simon
Author_Institution
Dipt. di Ing. Elettron. e delle Telecomun., Univ. degli Studi di Napoli Federico II, Naples, Italy
fYear
1998
fDate
8-11 Sept. 1998
Firstpage
1
Lastpage
4
Abstract
The recently introduced Support Vector Method (SVM) is one of the most powerful methods for training a Radial Basis Function (RBF) filter in a batch mode. This paper proposes a modification of this method for on-line adaptation of the filter parameters on a block-by-block basis. The proposed method requires a limited number of computations and compares well with other adaptive RBF filters.
Keywords
adaptive filters; nonlinear filters; support vector machines; adaptive nonlinear filtering; on-line adaptation; radial basis function filter; support vector method; Accuracy; Approximation algorithms; Approximation methods; Convergence; Support vector machines; Time series analysis; Training;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing Conference (EUSIPCO 1998), 9th European
Conference_Location
Rhodes
Print_ISBN
978-960-7620-06-4
Type
conf
Filename
7090060
Link To Document