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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.09361 |
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Table of Contents:
- A nonlinear kernel-free soft quadratic surface support vector machine model with 0-1 loss function ($L_{0/1}$-SQSSVM) is proposed for binary classification problems, which is non-convex discontinuous. We are devoted to establishing the first and the second-order optimality conditions for the $L_{0/1}$-SQSSVM. We establish a stationary equation using the properties of proximal operator of 0-1 loss function. We design a Newton method based on the stationary equation to solve $L_{0/1}$-SQSSVM model and prove that the Newton method has local quadratic convergence under the second-order sufficient condition. Numerical experience on artificial datasets and benchmark datasets demonstrate that the Newton method for $L_{0/1}$-SQSSVM achieves higher classification accuracy with less CPU time cost than other state-of-the-art methods.