A Note on Fuzzy Support Vector Classification

Title & Authors
A Note on Fuzzy Support Vector Classification
Lee, Sung-Ho; Hong, Dug-Hun;

Abstract
The support vector machine has been well developed as a powerful tool for solving classification problems. In many real world applications, each training point has a different effect on constructing classification rule. Lin and Wang (2002) proposed fuzzy support vector machines for this kind of classification problems, which assign fuzzy memberships to the input data and reformulate the support vector classification. In this paper another intuitive approach is proposed by using the fuzzy $\small{{\alpha}-cut}$ set. It will show us the trend of classification functions as $\small{{\alpha}}$ changes.
Keywords
SVM;SVC;fuzzy membership;$\small{{\alpha}-cut}$ set;
Language
English
Cited by
References
1.
Cortes, C. and Vapnik, V. (1995). Support vector network. Machine Learning, 20, 273-297

2.
Gunn, S. (1998). Support vector machines for classification and regression. Technical report, Image Speech and Intelligent Systems Research Group, University of Southampton

3.
Lin, C. F. and Wang, S. D. (2002). Fuzzy support vector machines. IEEE Transactions on Neural Networks, 13, 464-471

4.
Mercer, J. (1909). Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transaction of the Royal Society of London, Ser. A, 209, 415-446

5.
Scholkopf, B. and Smola, A. J. (2002). Learning with Kernels. MIT Press, Cambridge, MA

6.
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer-Verlag, Berlin

7.
Vapnik, V. N. (1998). Statistical Learning Theory. Wiley-Interscience, New York

8.
Vapnik, V. N. and Chervonenkis, A. J. (1964). A note on a class of perceptrons. Automation and Remote Control, 25, 112-120

9.
Vapnik, V. and Lerner. L. (1963). Pattern Recognition using generalized portrait method. Automation and Remote Control, 24, 774-780