Least squares support vector machines based on support vector degrees

Authors:
Lijuan Li;Youfeng Li;Hongye Su;Jian Chu
Affiliations:
National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, Hangzhou, P.R. China;National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, Hangzhou, P.R. China;National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, Hangzhou, P.R. China;National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, Hangzhou, P.R. China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

Citing 5
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Modeling pH neutralization processes using fuzzy-neural approaches

Fuzzy Sets and Systems
Least Squares Support Vector Machine Classifiers

Neural Processing Letters
Training algorithms for fuzzy support vector machines with noisy data

Pattern Recognition Letters
Fuzzy support vector machines

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

A modified least squares support vector machines (LS-SVM) approach, which treats the training data points differently according to their different degrees of importance, is proposed in this paper. On each data point, a support vector degree is defined and it is associated with the corresponding absolute value of Lagrange multiplier. The experiment of identification of pH neutralization process with polluted measuring data is shown in this paper and the result indicates that the method is effective in identification of nonlinear system. By contrast with the basic LS-SVM, the result also shows the priority of the presented new algorithm.