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dc.contributor.author김용대-
dc.date.accessioned2018-06-02T08:13:43Z-
dc.date.available2018-06-02T08:13:43Z-
dc.date.issued2002-
dc.identifier.isbn0769517544-
dc.identifier.isbn9780769517544-
dc.identifier.issn1550-4786-
dc.identifier.otherOAK-18031-
dc.identifier.urihttp://dspace.ewha.ac.kr/handle/2015.oak/243847-
dc.description.abstractWe propose a new ensemble algorithm called "Convex Hull Ensemble Machine (CHEM)." CHEM in Hilbert space is developed first and it is modified to regression and classification problems. Empirical studies show that in classification problems CHEM has similar prediction accuracy as AdaBoost, but CHEM is much more robust to output noise. In regression problems, CHEM work competitively with other ensemble methods such as Gradient Boost and Bagging. © 2002 IEEE.-
dc.description.sponsorshipIEEE Comput. Soc. Tech. Comm. Pattern;Anal. Mach. Intell. (TCPAMI);IEEE Comput. Soc. Tech. Comm. Comput. Intell. (TCCI)-
dc.languageEnglish-
dc.titleConvex hull ensemble machine-
dc.typeConference Paper-
dc.relation.indexSCOPUS-
dc.relation.startpage243-
dc.relation.lastpage249-
dc.relation.journaltitleProceedings - IEEE International Conference on Data Mining, ICDM-
dc.identifier.scopusid2-s2.0-62649083059-
dc.author.googleKim Y.-
dc.date.modifydate20180601095614-
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자연과학대학 > 통계학전공 > Journal papers
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