{"type":"ScholarlyArticle","title":"On Gleaning Knowledge from Multiple Domains for Active Learning","authors":[{"name":"Zengmao Wang","slug":"zengmao-wang","given":"Zengmao","family":"Wang","affiliation":"School of Computer , Wuhan University"},{"name":"Bo Du","slug":"bo-du","given":"Bo","family":"Du","affiliation":"School of Computer , Wuhan University"},{"name":"Lefei Zhang","slug":"lefei-zhang","given":"Lefei","family":"Zhang","affiliation":"School of Computer , Wuhan University"},{"name":"Liangpei Zhang","slug":"liangpei-zhang","given":"Liangpei","family":"Zhang","affiliation":"State Key Laboratory of Information Engineering in Surveying , Mapping and Remote Sensing"},{"name":"Ruimin Hu","slug":"ruimin-hu","given":"Ruimin","family":"Hu","affiliation":"School of Computer , Wuhan University; National Engineering Research Center for Multimedia Software , School of Computer , Wuhan University"},{"name":"Dacheng Tao","slug":"dacheng-tao","given":"Dacheng","family":"Tao","affiliation":"UBTech Sydney AI Institute , The School of Information Technologies , The University of Sydney"}],"abstract":"How can a doctor diagnose new diseases with little historical knowledge, which are emerging over time? Active learning is a promising way to address the problem by querying the most informative samples. Since the diagnosed cases for new disease are very limited, gleaning knowledge from other domains (classical prescriptions) to prevent the bias of active leaning would be vital for accurate diagnosis. In this paper, a framework that attempts to glean knowledge from multiple domains for active learning by querying the most uncertain and representative samples from the target domain and calculating the importance weights for re-weighting the source data in a single unified formulation is proposed. The weights are optimized by both a supervised classifier and distribution matching between the source domain and target domain with maximum mean discrepancy. Besides, a multiple domains active learning method is designed based on the proposed framework as an example. The proposed method is verified with newsgroups and handwritten digits data recognition tasks, where it outperforms the state-of-the-art methods.","doi":"10.24963/ijcai.2017/420","url":"https://ijcai.org/proceedings/2017/420","pdf":"https://ijcai.org/proceedings/2017/0420.pdf","markdown":"https://ijcai.org/proceedings/2017/420.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/420","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":420,"pages":{"first":3013,"last":3019},"keywords":["Machine Learning: Machine Learning","Machine Learning: Active Learning","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Bruzzone and Marconcini, 2010] L. 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