{"type":"ScholarlyArticle","title":"Optimal and Adaptive Algorithms for Online Boosting","authors":[{"name":"Alina Beygelzimer","slug":"alina-beygelzimer","given":"Alina","family":"Beygelzimer","affiliation":""},{"name":"Satyen Kale","slug":"satyen-kale","given":"Satyen","family":"Kale","affiliation":""},{"name":"Haipeng Luo","slug":"haipeng-luo","given":"Haipeng","family":"Luo","affiliation":""}],"abstract":"We study online boosting, the task of converting any weak online learner into a strong online learner. Based on a novel and natural definition of weak online learnability, we develop two online boosting algorithms. The first algorithm is an online version of boost-by-majority. By proving a matching lower bound, we show that this algorithm is essentially optimal in terms of the number of weak learners and the sample complexity needed to achieve a specified accuracy. The second algorithm is adaptive and parameter-free, albeit not optimal.","doi":null,"url":"https://ijcai.org/proceedings/2016/614","pdf":"https://ijcai.org/proceedings/2016/0614.pdf","markdown":"https://ijcai.org/proceedings/2016/614.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/614","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":614,"pages":{"first":4120,"last":4123},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}