Title :
Estimation of the regularization parameter for support vector regression
Author :
Jordaan, E.M. ; Smits, G.F.
Author_Institution :
Dept. of Math. & Comput. Sci., Eindhoven Univ. of Technol., Netherlands
fDate :
6/24/1905 12:00:00 AM
Abstract :
Support vector machines use a regularization parameter C to regulate the trade-off between the complexity of the model and the empirical risk of the model. Most of the techniques available for determining the optimal value of C are very time consuming. For industrial applications of the SVM method, there is a need for a fast and robust method to estimate C. A method based on the characteristics of the kernel, the range of output values and the size of the ε-insensitive zone, is proposed
Keywords :
learning automata; parameter estimation; quadratic programming; statistical analysis; ϵ-insensitive zone; complexity; empirical risk; kernel; regularization parameter; robust estimation; support vector machines; support vector regression; Computer science; Kernel; Lagrangian functions; Machine learning; Materials science and technology; Mathematics; Robustness; Support vector machine classification; Support vector machines; Training data;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007481
