Sparse $ε$ insensitive zone bounded asymmetric elastic net support vector machines for pattern classification
Existing support vector machines(SVM) models are sensitive to noise and lack sparsity, which limits their performance. To address these issues, we combine the elastic net loss with a robust loss framework to construct a sparse $\varepsilon$-insensitive bounded asymmetric elastic net loss, and integrate it with SVM to build $\varepsilon$ Insensitive Zone Bounded Asymmetric Elastic Net Loss-based SVM($\varepsilon$-BAEN-SVM). $\varepsilon$-BAEN-SVM is both sparse and robust. Sparsity is proven by showing that samples inside the $\varepsilon$-insensitive band are not support vectors. Robustness is theoretically guaranteed because the influence function is bounded. To solve the non-convex opti...
| Authors | Haiyan Du, Hu Yang |
|---|---|
| Primary category | stat.ML |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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