A two-level approach to choose the cost parameter in support vector machines

Authors:
Yulin Dong;Zhonghang Xia;Zunquan Xia
Affiliations:
Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, China;Department of Computer Science, Western Kentucky University, KY42101, USA;Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

Citing 8
Cited 0

Practical methods of optimization; (2nd ed.)

Practical methods of optimization; (2nd ed.)
The nature of statistical learning theory

The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
SSVM: A Smooth Support Vector Machine for Classification

Computational Optimization and Applications
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Training Support Vector Machines: an Application to Face Detection

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Recommender systems using linear classifiers

The Journal of Machine Learning Research
The Entire Regularization Path for the Support Vector Machine

The Journal of Machine Learning Research

Quantified Score

Hi-index	12.06

Visualization

Abstract

A new SVM model used to calculate the optimal value of cost parameter C for particular problems of linearity non-separability of data is presented in this paper. The new SVM model is formulated in the form of one of MPEC problems with an integer objective function. A lower bound, positive number, C"0 is required to provide for avoiding choosing a candidate set of C. Numerical experiments show that this model for choice of C is suitable for solving SVM problems.