{"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/support-vector-machines-for-multiple-instance","title":"Support Vector Machines for Multiple-Instance Learning","arxiv_id":null,"date":"2002-01-01","proceeding":"Advances in Neural Information Processing Systems 2002 1","authors":["Stuart Andrews","Ioannis Tsochantaridis","Thomas Hofmann"],"abstract":"This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions\r\nof the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristically. Our generalization of SVMs makes a state-of-the-art classification technique, including non-linear classification via kernels, available to an area that up to now has been largely dominated by special purpose methods. We present experimental results on a pharmaceutical data set and on applications in automated image indexing and document categorization.","url_abs":"https://papers.nips.cc/paper_files/paper/2002/hash/3e6260b81898beacda3d16db379ed329-Abstract.html","url_pdf":"https://papers.nips.cc/paper_files/paper/2002/file/3e6260b81898beacda3d16db379ed329-Paper.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":[],"tasks":[{"task_slug":"multiple-instance-learning","task_name":"Multiple Instance Learning"}],"methods":[],"datasets_introduced":[{"slug":"elephant","name":"Elephant","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}