{"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/algebraic-multigrid-support-vector-machines","title":"Algebraic multigrid support vector machines","arxiv_id":"1611.05487","date":"2016-11-16","proceeding":null,"authors":["Ehsan Sadrfaridpour","Sandeep Jeereddy","Ken Kennedy","Andre Luckow","Talayeh Razzaghi","Ilya Safro"],"abstract":"The support vector machine is a flexible optimization-based technique widely\nused for classification problems. In practice, its training part becomes\ncomputationally expensive on large-scale data sets because of such reasons as\nthe complexity and number of iterations in parameter fitting methods,\nunderlying optimization solvers, and nonlinearity of kernels. We introduce a\nfast multilevel framework for solving support vector machine models that is\ninspired by the algebraic multigrid. Significant improvement in the running has\nbeen achieved without any loss in the quality. The proposed technique is highly\nbeneficial on imbalanced sets. We demonstrate computational results on publicly\navailable and industrial data sets.","url_abs":"http://arxiv.org/abs/1611.05487v2","url_pdf":"http://arxiv.org/pdf/1611.05487v2.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":"algebraic-multigrid-support-vector-machines","repo_url":"https://github.com/esadr/mlsvm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}