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dc.contributor.author김영훈-
dc.date.accessioned2019-05-07T04:45:58Z-
dc.date.available2019-05-07T04:45:58Z-
dc.date.issued2017-07-
dc.identifier.citation2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Page. 2736-2739en_US
dc.identifier.isbn978-1-5090-2809-2-
dc.identifier.issn1558-4615-
dc.identifier.urihttps://ieeexplore.ieee.org/document/8037423-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/103484-
dc.description.abstractMotif detection, which is to discover short patterns involved in many important biological processes, has been recently raised as an important task in bioinformatics. The traditional algorithms to find a sequence motif have been developed using machine learning only without involving the experience and domain knowledge of human experts effectively. In this paper, we propose an interactive motif discovery system by introducing a new learning algorithm, by generalizing a well-known statistical motif model, whose inference can be shepherded by human feedback. © 2017 IEEE.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.titleActiveMotif: Interactive Motif Discovery with Human Feedbacken_US
dc.typeArticleen_US
dc.identifier.doi10.1109/EMBC.2017.8037423-
dc.relation.page2736-2739-
dc.contributor.googleauthorKim, Y-
dc.contributor.googleauthorLee, W-
dc.contributor.googleauthorKim, K-
dc.relation.code20170103-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF COMPUTING[E]-
dc.sector.departmentDIVISION OF COMPUTER SCIENCE-
dc.identifier.pidnongaussian-
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COLLEGE OF COMPUTING[E](소프트웨어융합대학) > COMPUTER SCIENCE(소프트웨어학부) > Articles
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