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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제21권 제4호
- 발행연도
- 2019.1
- 수록면
- 1,645 - 1,653 (9page)
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초록· 키워드
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Domain adaptation aims to predict a response variable accurately in a target domain where labeled instances are non-exist or scarce, if any, but unlabeled instances are plentiful, by utilizing as much as possible abundant labeled information in one or several related source domains. Therefore it can be considered as a transfer learning which is one of hot topics in machine learning community. Although a target domain is related to source domains in any way, the underlying distributions generating instances are different. This inevitably carries on the so called concept drift phenomenon which means the input-output dependency changes across the domains. Under the concept drift, many ensemble learning algorithms have been suggested and showed quite good results. In this paper, we apply an ensemble learning scheme for semi-supervised domain adaptation as in the concept drift learning where a penalized regression based ensemble combiner is utilized. The proposed method is applied to a sentiment classification and shows a good result.
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참고문헌 (17)
참고문헌 신청
Ben-David, S. / 2010 / A theory of learning from different domains / Machine Learning 79:151~175
Blitzer, J. / 2007 / Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification / Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics :440~447
Dai, W. / 2007 / Boosting for transfer learning / Proceedings of the 24th International Conference on Machine Learning :193~200
Ditzler, G. / 2014 / Domain adaptation bounds for multiple expert systems under concept drift / International Joint Conference on Neural Networks :595~601
Freund, Y. / 1997 / A decision-theoretic generalization of on-line learning and an application to boosting / Journal of Computer and System Science 55(1):119~139
Blitzer, J. / 2007 / Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification / Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics :440~447
Dai, W. / 2007 / Boosting for transfer learning / Proceedings of the 24th International Conference on Machine Learning :193~200
Ditzler, G. / 2014 / Domain adaptation bounds for multiple expert systems under concept drift / International Joint Conference on Neural Networks :595~601
Freund, Y. / 1997 / A decision-theoretic generalization of on-line learning and an application to boosting / Journal of Computer and System Science 55(1):119~139
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