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Volumn 2, Issue , 2008, Pages 683-688

Active learning for pipeline models

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNINGS; ADAPTIVE STRATEGIES; COMPLEX APPLICATIONS; DATA REQUIREMENTS; EMPIRICAL RESULTS; LOCAL ACTIVES; MACHINE LEARNINGS; PIPELINE MODELS; RELATION EXTRACTIONS; SEQUENTIAL STAGES; SIGNIFICANT REDUCTIONS;

EID: 57749169475     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (19)

References (14)
  • 8
    • 57749179233 scopus 로고    scopus 로고
    • Jones, R. 2005. Learning to Extract Entities from Labeled and Unlabeled Text. Ph.D. Dissertation, Carnegie Mellon. Roth, D., and Small, K. 2006. Margin-based active learning for structured output spaces. In Proc. of the European Conference on Machine Learning (ECML).
    • Jones, R. 2005. Learning to Extract Entities from Labeled and Unlabeled Text. Ph.D. Dissertation, Carnegie Mellon. Roth, D., and Small, K. 2006. Margin-based active learning for structured output spaces. In Proc. of the European Conference on Machine Learning (ECML).
  • 10
    • 37849051438 scopus 로고    scopus 로고
    • Global inference for entity and relation identification via a linear programming formulation
    • Roth, D., and Yih, W.-T. 2007. Global inference for entity and relation identification via a linear programming formulation. In Introduction to Statistical Relational Learning.
    • (2007) Introduction to Statistical Relational Learning
    • Roth, D.1    Yih, W.-T.2
  • 14
    • 0042868698 scopus 로고    scopus 로고
    • Support vector machine active learning with applications to text classification
    • Tong, S., and Roller, D. 2001. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research 2: 45-66.
    • (2001) Journal of Machine Learning Research , vol.2 , pp. 45-66
    • Tong, S.1    Roller, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.