Abstract
This paper based on the concept of function interpolation, a functional network interpolation mechanism was analyzed, the equivalent between functional network and kernel functions based SVM, and the equivalent relationship between functional networks with SVM is demonstrated. This result provides us a very useful guideline when we perform theoretical research and applications on design SVM, functional network systems.
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References
Castillo, E.: Function Networks. Neural Processing Letters 7, 151–159 (1998)
Castillo, E., Gutierrez, J.M.: Nonlinear Time Series Modeling And Prediction Using Functional Networks. Extracting Information Masked By Chaos. Physics Letters A 244, 71–84 (1998)
Castillo, E., Cobo, A., Gutierrez, J.M.: Working With Differential And Difference Equations Using Functional Networks. Applied Mathematical Modeling 23, 89–107 (1999)
Oscar, F.R., Enrique, C., Amparo, A.B.: A Measure of Fault Tolerance for Functional Networks. Neurocomputing 62, 327–347 (2004)
Castillo, E., Hadi, A., Locruz, B.: Semi-Parametric Nonlinear Regression And Transformating Using Functional Networks. Computational Statistics & Data Analysis 52, 2129–2157 (2008)
Zhou, Y.Q., Zhao, B., Jiao, L.C.: Approximate Factorization Learning Algorithm of Multivariate Polynomials Based On Functional Networks. Journal of Information and Computational Science 2, 205–210 (2005)
Zhou, Y.-Q., Jiao, L.-c.: Interpolation Mechanism of Functional Networks. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 45–51. Springer, Heidelberg (2005)
Zhou, Y.Q., Zhao, B., Jiao, L.C.: Universal Learning Algorithm of Hierarchical Function Networks. Chinese Journal of Computers 28, 1277–1286 (2005)
Zhou, Y.Q., Zhao, B., Jiao, L.C.: Serial Function Networks Method And Learning Algorithm With Applications. Chinese Journal of Computers 31, 1074–1081 (2008)
Qu, H.B., Hu, B.G.: Variational Learning for Generalized Associative Functional Networks in Modeling Dynamic Process of Plant Growth. Ecological Informatics 4, 163–176 (2004)
Dossevi, A., Cosmelli, D., Garnero, L., Ammari, H.: Multivariate Reconstruction of Functional Networks From Cortical Souces Dynamics in MRI. IEEE Transactions on Biomedi-cal Engineering 55, 2074–2086 (2008)
Emad, A., El-Sebakhy: Software reliability identification using functional networks. A comparative study. Expert Syst. Appl. 36, 4013–4020 (2009)
Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)
Li, Y.G., Zhang, F., Liu, Z.Y.: Combining Position-Specific-Value Method and SVM for Remote Protein Classification. Computer Science 31, 44–50 (2008)
Zhang, L.: The Relationship Between Kernel Function Based SVM and Three-Layer Feedforward Neural Networks. Computer Science 25, 696–700 (2002)
Guo, P., Zhou, Y., Xiao, Q.: Expansion Type Functional Neuron Network Model and its Parameters to Directly Determine the Method. Journal of Software 8, 443–450 (2013)
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Zhou, Y., Luo, Q., Ma, M., Li, L. (2014). The Equivalence Relationship between Kernel Functions Based on SVM and Four-Layer Functional Networks. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_10
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DOI: https://doi.org/10.1007/978-3-319-09339-0_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09338-3
Online ISBN: 978-3-319-09339-0
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