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Convex losses and their applications to SVM, SVR, and Shallow Neural Networks — arXiv:2608.14288

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.14288
License (as archived): CC0-1.0
Archived: 2026-10-19T02:30:37.754Z
SHA-256 of the archived text: 3e1c21ef23713548205715dc1c6cb031a1717e47482deacefc07f9fb9e9b04b8
Stable URL of this page: https://metraipolis.com/documento/dc_fd77b896d481

How to cite

Convex losses and their applications to SVM, SVR, and Shallow Neural Networks — arXiv:2608.14288 — arXiv (en), https://arxiv.org/abs/2608.14288 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fd77b896d481 (Metraipolis archive dc_fd77b896d481, sha256 3e1c21ef23713548205715dc1c6cb031a1717e47482deacefc07f9fb9e9b04b8)

BibTeX

@misc{dcfd77b896d481,
  title = {Convex losses and their applications to SVM, SVR, and Shallow Neural Networks — arXiv:2608.14288},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.14288},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fd77b896d481; sha256 3e1c21ef23713548205715dc1c6cb031a1717e47482deacefc07f9fb9e9b04b8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.14288. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We propose multiple new convex losses for SVM and Neural Networks, applied to binary classification tasks. While there are practical limitations in exploiting them with the dual SVM models, we are able to use them with SVM primal formulation and Neural Networks. In detail, the primal SVM problem with the modified losses has been solved with the Particle Swarm Optimization algorithm. We prove that the proposed losses are a generalization of the standard loss, and we experiment them with several small data-sets. This preliminary study shows that using pattern correlations inside the loss function could in theory enhance the generalization performances on some data-sets. To evaluate the performance of each loss, we adopt a Nested Cross-Validation procedure. Results show that generalization measures are the same with or without the new losses.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-17 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.14288 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T02:26:17.799Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).