Comparison of SVM and LS-SVM for Regression

Support vector machines (SVM) has been widely used in classification and nonlinear function estimation. However, the major drawback of SVM is its higher computational burden for the constrained optimization programming. This disadvantage has been overcome by least squares support vector machines (LS...

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Bibliographic Details
Main Authors: Haifeng Wang, Dejin Hu
Format: Conference Proceeding
Language:English
Subjects:
Constraint optimization
Equations
Lagrangian functions
Least squares approximation
Least squares methods
Linear systems
Power engineering
Support vector machine classification
Support vector machines
Testing
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Summary:Support vector machines (SVM) has been widely used in classification and nonlinear function estimation. However, the major drawback of SVM is its higher computational burden for the constrained optimization programming. This disadvantage has been overcome by least squares support vector machines (LS-SVM), which solves linear equations instead of a quadratic programming problem. This paper compares LS-SVM with SVM for regression. According to the parallel test results, conclusions can be made that LS-SVM is preferred especially for large scale problem, because its solution procedure is high efficiency and after pruning both sparseness and performance of LS-SVM are comparable with those of SVM
DOI:10.1109/ICNNB.2005.1614615