Abstract
The paper proposes an edge-effect training multi-class fuzzy support vector machine (EFSVM). It treats the training data points with different importance in the training process, and especially emphasizes primary contribution of these points distributed in edge area of data sets for classification, and then assigns them greater fuzzy membership degrees, thus assures that the nearer these points are away from edge area of training sets and the greater their contribution are. At the same time EFSVM is systematically compared to two other fuzzy support vector machines and a Levenberg-Marquardt-based BP algorithm (LMBP). The classification results for both Iris data and remote sensing image show that EFSVM is the best and may effectively enhance pattern classification accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Atkinson, P.M., Tatnall, A.R.L.: Neural Networks in Remote Sensing. Int. J. Remote Sensing. 18(4), 699–709 (1997)
Paola, J.D., Schowengerdt, R.A.: A Review and Analysis of Back-propagation Neural Networks for Classification of Remotely Sensed Multi-spectral Image. Int. J. GIS. 16(16), 3033–3058 (1995)
Vapnik, V.N.: Statistical Learning theory. Wiley, New York (1998)
Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20(3), 273–297 (1995)
Burges, J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining 2(2), 121–167 (1998)
Kanevski, M., Pozdnukhov, A., Canu, S., Maignan, M.: Advanced Spatial Data Analysis and Modeling with Support Vector Machines. International Journal of Fuzzy Systems 4(1), 606–615 (2002)
Zhang, X.: Using Class-Center Vectors to Build Support Vector Machines. In: Proc. IEEE NNSP 1999, pp. 3–11 (1999)
Guyon, I., Matic, N., Vapnik, V.N.: Discovering Information Patterns and Data Cleaning, pp. 181–203. MIT Press, Cambridge (1996)
Hung, H.P., Liu, Y.H.: Fuzzy Support Vector Machines for Pattern Recognition and Data Mining. International Journal of Fuzzy Systems 4(3), 826–835 (2002)
Li, C.F., Wang, Z.Y.: Remote Sensing Image Classification Method Based on Support Vector Machines and Fuzzy Membership Function. In: MIPPR 2005: SAR and Multispectral Image Processing, vol. 7, pp. 604324-1–7. SPIE, Wuhan (2005)
Hsu, C.W., Lin, C.J.: A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transactions on Neural Networks 13(2), 415–425 (2002)
Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large Margin DAG’s for Multiclass Classification. In: Advances in Neural Information Processing Systems, vol. 12, pp. 547–553. MIT Press, Cambridge (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Cf., Xu, L., Wang, St. (2006). A Comparative Study on Improved Fuzzy Support Vector Machines and Levenberg-Marquardt-Based BP Network. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_8
Download citation
DOI: https://doi.org/10.1007/11816157_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
eBook Packages: Computer ScienceComputer Science (R0)