An efficient algorithm to improve the accuracy and reduce the computations of LS-SVM

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Baymani M.; Mansoori A.

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Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF NUMERICAL ANALYSIS AND OPTIMIZATION Year:2020 | Volume:10 | Issue:1 Page(s): 33-47

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Title

An efficient algorithm to improve the accuracy and reduce the computations of LS-SVM

Author(s)

Baymani M. | Mansoori A. | Issue Writer Certificate

Keywords

Least squares support vector machine

Cutting algorithm

Classification

Abstract

We present a novel algorithm, which is called Cutting algorithm (CA), for improving the accuracy and reducing the computations of the Least squares support vector machines (LS-SVMs). The method is based on dividing the original problem to some subproblems. Since a master problem is converted to some small problems, so this algorithm has fewer computations. Although, in some cases that the typical LS-SVM cannot classify the dataset linearly, applying the CA the datasets can be classified. In fact, the CA improves the accuracy and reduces the computations. The reported and comparative results on some known datasets and synthetics data demonstrate the efficiency and the performance of CA.

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