{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multiclass-universum-svm","title":"Multiclass Universum SVM","arxiv_id":"1808.08111","date":"2018-08-23","proceeding":null,"authors":["Sauptik Dhar","Vladimir Cherkassky","Mohak Shah"],"abstract":"We introduce Universum learning for multiclass problems and propose a novel\nformulation for multiclass universum SVM (MU-SVM). We also propose an analytic\nspan bound for model selection with almost 2-4x faster computation times than\nstandard resampling techniques. We empirically demonstrate the efficacy of the\nproposed MUSVM formulation on several real world datasets achieving > 20%\nimprovement in test accuracies compared to multi-class SVM.","url_abs":"http://arxiv.org/abs/1808.08111v1","url_pdf":"http://arxiv.org/pdf/1808.08111v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multiclass-universum-svm","repo_url":"https://github.com/LGE-ARC-AdvancedAI/MU-SVM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}