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Volumn , Issue , 2007, Pages 823-829

Optimistic active learning using mutual information

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; ACTIVE LEARNING SYSTEMS; CLASSIFICATION BOUNDARY; CONDITIONAL MUTUAL INFORMATION; MUTUAL INFORMATIONS; QUERY SELECTION; SELECTION CRITERIA; SELECTION RULES;

EID: 84880855398     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (162)

References (19)
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  • 2
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    • Query learning with large margin classifiers
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  • 6
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    • Very simple classification rules perform well on most commonly used datasets
    • Robert C. Holte. Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11:63-91, 1993.
    • (1993) Machine Learning , vol.11 , pp. 63-91
    • Holte, R.C.1
  • 7
    • 85013879626 scopus 로고
    • A sequential algorithm for training text classifiers
    • D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In ACM-SIGIR, 1994.
    • (1994) ACM-SIGIR
    • Lewis, D.1    Gale, W.2
  • 8
    • 0000314722 scopus 로고    scopus 로고
    • Employing em in pool-based active learning for text classification
    • A. McCallum and K. Nigam. Employing em in pool-based active learning for text classification. In ICML, 1998.
    • (1998) ICML
    • McCallum, A.1    Nigam, K.2
  • 10
    • 3242788638 scopus 로고    scopus 로고
    • Active + semi-supervised learning = robust multi-view learning
    • I. Muslea, S. Minton, and C. Knoblock. Active + semi-supervised learning = robust multi-view learning. In ICML, 2002.
    • (2002) ICML
    • Muslea, I.1    Minton, S.2    Knoblock, C.3
  • 11
    • 14344265134 scopus 로고    scopus 로고
    • Active learning using pre-clustering
    • H. Nguyen and A. Smeulders. Active learning using pre-clustering. In ICML, 2004.
    • (2004) ICML
    • Nguyen, H.1    Smeulders, A.2
  • 12
    • 0442319140 scopus 로고    scopus 로고
    • Toward optimal active learning through sampling estimation of error reduction
    • N. Roy and A. McCallum. Toward optimal active learning through sampling estimation of error reduction. In ICML, 2001.
    • (2001) ICML
    • Roy, N.1    McCallum, A.2
  • 13
    • 84880911908 scopus 로고    scopus 로고
    • Active learning for multi-class logistic regression
    • Andrew I. Schein and Lyle H. Ungar. Active learning for multi-class logistic regression. In LEARNING 2005, 2005.
    • (2005) LEARNING 2005
    • Schein, A.I.1    Ungar, L.H.2
  • 14
    • 0007696417 scopus 로고    scopus 로고
    • Less is more: Active learning with support vector machines
    • G. Schohn and D. Cohn. Less is more: Active learning with support vector machines. In ICML, 2000.
    • (2000) ICML
    • Schohn, G.1    Cohn, D.2
  • 16
    • 0003007938 scopus 로고    scopus 로고
    • Support vector machine active learning with applications to text classification
    • S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In ICML, 2000.
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    • Tong, S.1    Koller, D.2
  • 17
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    • UCI, 2006. http://www.ics.uci.edu/~mlearn/MLSummary.html.
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  • 19
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    • An active learning framework for content-based information retrieval
    • C. Zhang and T. Chen. An active learning framework for content-based information retrieval. IEEE Trans on Multimedia, 4:260-258, 2002.
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    • Zhang, C.1    Chen, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.