{"type":"ScholarlyArticle","title":"A Direct Boosting Approach for Semi-supervised Classification","authors":[{"name":"Shaodan Zhai","slug":"shaodan-zhai","given":"Shaodan","family":"Zhai","affiliation":""},{"name":"Tian Xia","slug":"tian-xia","given":"Tian","family":"Xia","affiliation":""},{"name":"Zhongliang Li","slug":"zhongliang-li","given":"Zhongliang","family":"Li","affiliation":""},{"name":"Shaojun Wang","slug":"shaojun-wang","given":"Shaojun","family":"Wang","affiliation":""}],"abstract":"We introduce a semi-supervised boosting approach (SSDBoost), which directly minimizes the classification errors and maximizes the margins on both labeled and unlabeled samples, without resorting to any upper bounds or approximations. A two-step algorithm based on coordinate descent/ascent is proposed to implement SSDBoost. Experiments on a number of UCI datasets and synthetic data show that SSDBoost gives competitive or superior results over the state-of-the-art supervised and semi-supervised boosting algorithms in the cases that the labeled data is limited, and it is very robust in noisy cases.","doi":null,"url":"https://ijcai.org/proceedings/2015/565","pdf":"https://ijcai.org/proceedings/2015/0565.pdf","markdown":"https://ijcai.org/proceedings/2015/565.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/565","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Machine Learning","track":"Special Track on Machine Learning","number":565,"pages":{"first":4033,"last":4039},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}