연구동향 분석

An Ensemble Approach to Domain Adaptation in Sentiment Analysis

연규필 2019년

논문상세정보
인용/피인용
' An Ensemble Approach to Domain Adaptation in Sentiment Analysis' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • concept drift.
  • domainadaptation
  • ensemble learning
  • transfer learning
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
80 0

0.0%

' An Ensemble Approach to Domain Adaptation in Sentiment Analysis' 의 참고문헌

  • 한국자료분석학회지에 대한 토픽분석
    강창완 [2018]
  • 토픽모델링과 감성분석에 기반한 금통위 의사록 분석
    이영준 [2019]
  • 텍스트마이닝을 활용한 연준의 통화정책방향 의결문 분석
    우신욱 [2016]
  • 텍스트 마이닝을 활용한 개인정보유출 보고서의 군집 분석
    심현우 [2019]
  • Tracking concept drift using a constrained penalized regression combiner
    Wang, L. [2017]
  • Stacked generalization
  • Multi-source domain adaptation with mixture of experts
    Guo, J. [2018]
  • Model averaging via penalized regression for tracking concept drift
    Yeon, K. [2010]
  • Heterogeneous domain adaptation and classification by exploiting the correlation subspace
    Yeh, Y. [2014]
  • Feature ensemble plus sample selection : domain adaptation for sentiment classification
    Xia, R. [2013]
  • Dynamical ensemble learning with model-friendly classifiers for domain adaptation
    Tu, W. [2012]
  • Domain adaptation with ensemble of feature groups
    Samdani, R. [2011]
  • Domain adaptation bounds for multiple expert systems under concept drift
    Ditzler, G. [2014]
  • Boosting for transfer learning
    Dai, W. [2007]
  • Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
    Blitzer, J. [2007]
  • A theory of learning from different domains
  • A decision-theoretic generalization of on-line learning and an application to boosting
    Freund Y [1997]