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September 10, 2025ProcessesOpen Access

A Dual-Norm Support Vector Machine: Integrating L1 and L∞ Slack Penalties for Robust and Sparse Classification

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This approach integrates L1 and L∞ penalties in support vector machine classification, improving robustness and accuracy.

The proposed method effectively balances average performance and worst-case errors, enhancing classification robustness.
Experimental evaluations demonstrate that this dual-norm approach achieves superior accuracy while handling outliers effectively.
Incorporating both L1 and L∞ penalties enables the model to maintain a sparse structure alongside improved stability.
The optimization problem remains tractable under convex constraints, ensuring a globally optimal solution.

Liu et al. (2025) studied this question.

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September 10, 2025ProcessesOpen Access

View Full Paper

Ask AI

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Authors

This approach integrates L1 and L∞ penalties in support vector machine classification, improving robustness and accuracy.

The proposed method effectively balances average performance and worst-case errors, enhancing classification robustness.
Experimental evaluations demonstrate that this dual-norm approach achieves superior accuracy while handling outliers effectively.
Incorporating both L1 and L∞ penalties enables the model to maintain a sparse structure alongside improved stability.
The optimization problem remains tractable under convex constraints, ensuring a globally optimal solution.

Liu et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark