Abstract
A new algorithm for modeling regression curve is put forward in the paper, it combines the B-spline network with improved support vector regression. Our experimental results on simulated data demonstrate that it is feasible and effective.
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References
Vapnik V. Statistical learning theory[M]. New York: John Wiley & Sons, 1998.
Deng Nai Yang, Tian Ying Jie. A New Method in Data Mining—Support Vector Machine [M]. Science Press, 2004.6, 224–273.
Moody J. Fastlearning in multi-resolution hierarchies[J]. Advances in Neural information Processing System, vol.1, 1989:29–39.
Scholkopf B, Smola A J. Learning with Kernels Support Vector Machines, Regularization, Optimization, and Beyond[M].The MIT Press, 2002.
Martin Brown, Chris Harris. Neurofuzzy adaptive modeling and control [M]. Prentice Hall International (UK) Limited, 1994: 89–100.
Zhang HaoRan, Learing Algorithm for a New Regression SVM[J]. Journal of Test and Measurement Technology, Vol.20 No.2 2006
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© 2008 IFIP International Federation for Information Processing
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Hao, J., Ma, L., Wang, W. (2008). An New Algorithm for Modeling Regression Curve. In: Shi, Z., Mercier-Laurent, E., Leake, D. (eds) Intelligent Information Processing IV. IIP 2008. IFIP – The International Federation for Information Processing, vol 288. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-87685-6_12
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DOI: https://doi.org/10.1007/978-0-387-87685-6_12
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-87684-9
Online ISBN: 978-0-387-87685-6
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