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Volumn 22, Issue , 2012, Pages 422-431

UPAL: Unbiased pool based active learning

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; LAKES;

EID: 84908030865     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (33)

References (28)
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    • Query learning can work poorly when a human oracle is used
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    • Baum, E.B.1    Lang, K.2
  • 7
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    • Improving generalization with active learning
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    • (1994) Machine Learning , vol.15 , pp. 2
    • Cohn, D.1    Atlas, L.2    Ladner, R.3
  • 8
    • 56449123291 scopus 로고    scopus 로고
    • A general agnostic active learning algorithm
    • S. Dasgupta, D. Hsu, and C. Monteleoni. A general agnostic active learning algorithm. NIPS, 2007.
    • (2007) NIPS
    • Dasgupta, S.1    Hsu, D.2    Monteleoni, C.3
  • 11
    • 84880855398 scopus 로고    scopus 로고
    • Optimistic active learning using mutual information
    • Y. Guo and R. Greiner. Optimistic active learning using mutual information. In IJCAI, 2007.
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    • Guo, Y.1    Greiner, R.2
  • 12
    • 33749263388 scopus 로고    scopus 로고
    • Batch mode active learning and its application to medical image classification
    • S.C.H. Hoi, R. Jin, J. Zhu, and M.R. Lyu. Batch mode active learning and its application to medical image classification. In ICML, 2006.
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    • Hoi, S.C.H.1    Jin, R.2    Zhu, J.3    Lyu, M.R.4
  • 17
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    • A sequential algorithm for training text classifiers
    • D.D. Lewis and W.A. Gale. A sequential algorithm for training text classifiers. In SIGIR, 1994.
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    • Lewis, D.D.1    Gale, W.A.2
  • 18
    • 0000314722 scopus 로고    scopus 로고
    • Employing em and pool-based active learning for text classification
    • A.K. McCallum and K. Nigam. Employing EM and pool-based active learning for text classification. In ICML, 1998.
    • (1998) ICML
    • McCallum, A.K.1    Nigam, K.2
  • 20
    • 80053375448 scopus 로고    scopus 로고
    • An analysis of active learning strategies for sequence labeling tasks
    • B. Settles and M. Craven. An analysis of active learning strategies for sequence labeling tasks. In EMNLP, 2008.
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    • Settles, B.1    Craven, M.2
  • 21
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    • Statistical behavior and consistency of classification methods based on convex risk minimization
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  • 27
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    • The value of unlabeled data for classification problems
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    • Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.