{"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/learning-nonlinear-functions-using","title":"Learning Nonlinear Functions Using Regularized Greedy Forest","arxiv_id":"1109.0887","date":"2011-09-05","proceeding":null,"authors":["Rie Johnson","Tong Zhang"],"abstract":"We consider the problem of learning a forest of nonlinear decision rules with\ngeneral loss functions. The standard methods employ boosted decision trees such\nas Adaboost for exponential loss and Friedman's gradient boosting for general\nloss. In contrast to these traditional boosting algorithms that treat a tree\nlearner as a black box, the method we propose directly learns decision forests\nvia fully-corrective regularized greedy search using the underlying forest\nstructure. Our method achieves higher accuracy and smaller models than gradient\nboosting (and Adaboost with exponential loss) on many datasets.","url_abs":"http://arxiv.org/abs/1109.0887v7","url_pdf":"http://arxiv.org/pdf/1109.0887v7.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":"learning-nonlinear-functions-using","repo_url":"https://github.com/TimSalimans/HiggsML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1109.0887","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}