[2306.06213] A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2306.06213 (cs) [Submitted on 9 Jun 2023 ( v1 ), last revised 24 Jun 2025 (this version, v3)] Title: A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification Authors: Renato De Leone , Francesca Maggioni , Andrea Spinelli View a PDF of the paper titled A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification, by Renato De Leone and 1 other authors View PDF HTML (experimental) Abstract: In this paper, we introduce novel Twin Parametric Margin Support Vector Machine (TPMSVM) models designed to address multiclass classification tasks under feature uncertainty. To handle data perturbations, we construct bounded-by-norm uncertainty set around each training observation and derive the robust counterparts of the deterministic models using robust optimization techniques. To capture complex data structure, we explore both linear and kernel-induced classifiers, providing computationally tractable reformulations of the resulting robust models. Additionally, we propose two alternatives for the final decision function, enhancing models' flexibility. Finally, we validate the effectiveness of the proposed robust multiclass TPMSVM methodology on real-world datasets, showing the good performance of the approach in the presence of uncertainty. Subjects: Machine Learning (cs.LG) ; Optimization and Control (math.OC) Cite as: arXiv:2306.06213 [cs.LG] (or arXiv:2306.06213v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2306.06213 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1016/j.ejco.2025.100115 Focus to learn more DOI(s) linking to related resources Submission history From: Francesca Maggioni Prof. [ view email ] [v1] Fri, 9 Jun 2023 19:27:24 UTC (536 KB) [v2] Wed, 22 May 2024 11:58:19 UTC (1,201 KB) [v3] Tue, 24 Jun 2025 16:07:13 UTC (327 KB) Full-text links: Access Paper: View a PDF of the paper titled A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification, by Renato De Leone and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2023-06 Change to browse by: cs math math.OC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from