Advanced SearchSearch Tips
A Note on Fuzzy Support Vector Classification
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Note on Fuzzy Support Vector Classification
Lee, Sung-Ho; Hong, Dug-Hun;
  PDF(new window)
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 set. It will show us the trend of classification functions as changes.
SVM;SVC;fuzzy membership; set;
 Cited by
Cortes, C. and Vapnik, V. (1995). Support vector network. Machine Learning, 20, 273-297

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

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

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 crossref(new window)

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

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

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

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

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