{"type":"ScholarlyArticle","title":"Self-Paced Boost Learning for Classification","authors":[{"name":"Te Pi","slug":"te-pi","given":"Te","family":"Pi","affiliation":""},{"name":"Xi Li","slug":"xi-li-2","given":"Xi","family":"Li","affiliation":""},{"name":"Zhongfei Zhang","slug":"zhongfei-zhang","given":"Zhongfei","family":"Zhang","affiliation":""},{"name":"Deyu Meng","slug":"deyu-meng","given":"Deyu","family":"Meng","affiliation":""},{"name":"Fei Wu","slug":"fei-wu-3","given":"Fei","family":"Wu","affiliation":""},{"name":"Jun Xiao","slug":"jun-xiao","given":"Jun","family":"Xiao","affiliation":""},{"name":"Yueting Zhuang","slug":"yueting-zhuang","given":"Yueting","family":"Zhuang","affiliation":""}],"abstract":"Effectiveness and robustness are two essential aspects of supervised learning studies. For effective learning, ensemble methods are developed to build a strong effective model from ensemble of weak models. For robust learning, self-paced learning (SPL) is proposed to learn in a self-controlled pace from easy samples to complex ones. Motivated by simultaneously enhancing the learning effectiveness and robustness, we propose a unified framework, Self-Paced Boost Learning (SPBL). With an adaptive from-easy-to-hard pace in boosting process, SPBL asymptotically guides the model to focus more on the insufficiently learned samples with higher reliability. Via a max-margin boosting optimization with self-paced sample selection, SPBL is capable of capturing the intrinsic inter-class discriminative patterns while ensuring the reliability of the samples involved in learning. We formulate SPBL as a fully-corrective optimization for classification. The experiments on several real-world datasets show the superiority of SPBL in terms of both effectiveness and robustness.","doi":null,"url":"https://ijcai.org/proceedings/2016/276","pdf":"https://ijcai.org/proceedings/2016/0276.pdf","markdown":"https://ijcai.org/proceedings/2016/276.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/276","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":276,"pages":{"first":1932,"last":1937},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}