{"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/efficient-training-for-positive-unlabeled","title":"Efficient Training for Positive Unlabeled Learning","arxiv_id":"1608.06807","date":"2016-08-24","proceeding":null,"authors":["Emanuele Sansone","Francesco G. B. De Natale","Zhi-Hua Zhou"],"abstract":"Positive unlabeled (PU) learning is useful in various practical situations,\nwhere there is a need to learn a classifier for a class of interest from an\nunlabeled data set, which may contain anomalies as well as samples from unknown\nclasses. The learning task can be formulated as an optimization problem under\nthe framework of statistical learning theory. Recent studies have theoretically\nanalyzed its properties and generalization performance, nevertheless, little\neffort has been made to consider the problem of scalability, especially when\nlarge sets of unlabeled data are available. In this work we propose a novel\nscalable PU learning algorithm that is theoretically proven to provide the\noptimal solution, while showing superior computational and memory performance.\nExperimental evaluation confirms the theoretical evidence and shows that the\nproposed method can be successfully applied to a large variety of real-world\nproblems involving PU learning.","url_abs":"http://arxiv.org/abs/1608.06807v4","url_pdf":"http://arxiv.org/pdf/1608.06807v4.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":"efficient-training-for-positive-unlabeled","repo_url":"https://github.com/emsansone/USMO","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"learning-theory","task_name":"Learning Theory"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}