Single and Union Non-Parallel Support Vector Machine Frameworks

Li, Chun‐Na; Shao, Yuan-Hai; Wang, Huajun; Zhao, Yanguang; Huang, Ling-Wei; Xiu, Naihua; Deng, Naiyang

doi:10.2139/ssrn.4183367

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Support Vector Machine (Svm)1

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2023

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Cited by 1 publication

(2 citation statements)

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“…While the SVM has demonstrated superior performance compared with most other systems, it encounters limitations in dealing with complex data, primarily due to the high computational cost of solving quadratic programming problems (QPPs) and its strong reliance on the selection of kernel functions and their parameters. Notably, the past decade has seen significant advancements aimed at enhancing the accuracy of SVM. , In the SVM model, a nonlinear function is applied to the training data (eq ), enabling the model to estimate the dependent variables based on the independent variables . The equation provided here represents the formula for predicting the output of an SVM model.…”

Section: Methodsmentioning

confidence: 99%

“…Notably, the past decade has seen significant advancements aimed at enhancing the accuracy of SVM. 33,34 In the SVM model, a nonlinear function is applied to the training data (eq 1), enabling the model to estimate the dependent variables based on the independent variables. 35 The equation provided here represents the formula for predicting the output of an SVM model.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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