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Do Minimal Complexity Least Squares Support Vector Machines Work?

Abe, Shigeo

Artificial Neural Networks in Pattern Recognition, 2023, p.53-64 [Periódico revisado por pares]

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Título:
Do Minimal Complexity Least Squares Support Vector Machines Work?
Autor: Abe, Shigeo
É parte de: Artificial Neural Networks in Pattern Recognition, 2023, p.53-64
Descrição: The minimal complexity support vector machine is a fusion of the support vector machine (SVM) and the minimal complexity machine (MCM), and results in maximizing the minimum margin and minimizing the maximum margin. It works to improve the generalization ability of the L1 SVM (standard SVM) and LP (Linear Programming) SVM. In this paper, we discuss whether it also works for the LS (Least Squares) SVM. The minimal complexity LS SVM (MLS SVM) is trained by minimizing the sum of squared margin errors and minimizing the maximum margin. This results in solving a set of linear equations and a quadratic program, alternatingly. According to the computer experiments for two-class and multiclass problems, the MLS SVM does not outperform the LS SVM for the test data although it does for the cross-validation data.
Editor: Cham: Springer International Publishing
Idioma: Inglês

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