{"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/distributed-optimization-of-multi-class-svms","title":"Distributed Optimization of Multi-Class SVMs","arxiv_id":"1611.08480","date":"2016-11-25","proceeding":null,"authors":["Maximilian Alber","Julian Zimmert","Urun Dogan","Marius Kloft"],"abstract":"Training of one-vs.-rest SVMs can be parallelized over the number of classes\nin a straight forward way. Given enough computational resources, one-vs.-rest\nSVMs can thus be trained on data involving a large number of classes. The same\ncannot be stated, however, for the so-called all-in-one SVMs, which require\nsolving a quadratic program of size quadratically in the number of classes. We\ndevelop distributed algorithms for two all-in-one SVM formulations (Lee et al.\nand Weston and Watkins) that parallelize the computation evenly over the number\nof classes. This allows us to compare these models to one-vs.-rest SVMs on\nunprecedented scale. The results indicate superior accuracy on text\nclassification data.","url_abs":"http://arxiv.org/abs/1611.08480v2","url_pdf":"http://arxiv.org/pdf/1611.08480v2.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":"distributed-optimization-of-multi-class-svms","repo_url":"https://github.com/albermax/xcsvm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"}],"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}