{"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/learn-on-source-refine-on-targeta-model","title":"Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests","arxiv_id":"1511.01258","date":"2015-11-04","proceeding":null,"authors":["Noam Segev","Maayan Harel","Shie Mannor","Koby Crammer","Ran El-Yaniv"],"abstract":"We propose novel model transfer-learning methods that refine a decision\nforest model M learned within a \"source\" domain using a training set sampled\nfrom a \"target\" domain, assumed to be a variation of the source. We present two\nrandom forest transfer algorithms. The first algorithm searches greedily for\nlocally optimal modifications of each tree structure by trying to locally\nexpand or reduce the tree around individual nodes. The second algorithm does\nnot modify structure, but only the parameter (thresholds) associated with\ndecision nodes. We also propose to combine both methods by considering an\nensemble that contains the union of the two forests. The proposed methods\nexhibit impressive experimental results over a range of problems.","url_abs":"http://arxiv.org/abs/1511.01258v2","url_pdf":"http://arxiv.org/pdf/1511.01258v2.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":"learn-on-source-refine-on-targeta-model","repo_url":"https://github.com/Novemser/DSSM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"learn-on-source-refine-on-targeta-model","repo_url":"https://github.com/adapt-python/adapt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}