{"type":"ScholarlyArticle","title":"Monte Carlo Theory as an Explanation of Bagging and Boosting","authors":[{"name":"Roberto Esposito","slug":"roberto-esposito","given":"Roberto","family":"Esposito","affiliation":""},{"name":"Lorenza Saitta","slug":"lorenza-saitta","given":"Lorenza","family":"Saitta","affiliation":""}],"abstract":"In this paper we propose the framework of Monte Carlo algorithms as a useful one to analyze ensemble learning. In particular, this framework allows one to guess when bagging will be useful, explains why increasing the margin improves performances, and suggests a new way of performing ensemble learning and error estimation.","doi":null,"url":"https://ijcai.org/proceedings/2003/74","pdf":"https://ijcai.org/proceedings/2003/0074.pdf","markdown":"https://ijcai.org/proceedings/2003/74.md","bibtex":"https://ijcai.org/proceedings/2003/bibtex/74","volume":{"slug":"2003","title":"Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2003","url":"https://ijcai.org/proceedings/2003/","year":2003,"location":"Acapulco, Mexico","dates":"","editors":"Georg Gottlob","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":74,"pages":{"first":499,"last":503},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}