Nonlinear prediction of chaotic time series using support vectormachines
Mukherjee, S.; Osuna, E.; Girosi, F.
Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
Volume , Issue , 24-26 Sep 1997 Page(s):511 - 520
Digital Object Identifier   10.1109/NNSP.1997.622433
Summary:A novel method for regression has been recently proposed by Vapnik et al. (1995, 1996). The technique, called support vector machine (SVM), is very well founded from the mathematical point of view and seems to provide a new insight in function approximation. We implemented the SVM and tested it on a database of chaotic time series previously used to compare the performances of different approximation techniques, including polynomial and rational approximation, local polynomial techniques, radial basis functions, and neural networks. The SVM performs better than the other approaches. We also study, for a particular time series, the variability in performance with respect to the few free parameters of SVM

» View citation and abstract

Log in by entering your IEEE Web Account Username and Password.

IEEE Communications Society members: If you subscribe to the IEEE Electronic Periodicals Package or IEEE Electronic Periodicals Package Plus, you must access your subscription at www.comsoc.org.

Check with your librarian, information professional, or system manager to determine if you need to log in. Please complete the online Technical Support Form if you need assistance.

Select the Purchase History link to access the document. You will have 5 Days after purchase to access the Full Text PDF. Please complete the online Technical Support Form if you need assistance.

• Search and access Abstract records free of charge
• Register for table of contents alerts
• Purchase Full Text PDF documents

» Learn more about subscription options or how to become an IEEE Member.