{"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/muffled-semi-supervised-learning","title":"Muffled Semi-Supervised Learning","arxiv_id":"1605.08833","date":"2016-05-28","proceeding":null,"authors":["Akshay Balsubramani","Yoav Freund"],"abstract":"We explore a novel approach to semi-supervised learning. This approach is\ncontrary to the common approach in that the unlabeled examples serve to\n\"muffle,\" rather than enhance, the guidance provided by the labeled examples.\nWe provide several variants of the basic algorithm and show experimentally that\nthey can achieve significantly higher AUC than boosted trees, random forests\nand logistic regression when unlabeled examples are available.","url_abs":"http://arxiv.org/abs/1605.08833v1","url_pdf":"http://arxiv.org/pdf/1605.08833v1.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":"muffled-semi-supervised-learning","repo_url":"https://github.com/aikanor/marvin","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}