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Volumn , Issue , 2016, Pages 2180-2186

Noise-Adaptive margin-based active learning and lower bounds under tsybakov noise condition

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

EID: 85007235945     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (30)

References (24)
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    • A general agnostic active learning algorithm
    • Dasgupta, S.; Hsu, D.; and Monteleoni, C. 2007. A general agnostic active learning algorithm. In NIPS.
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    • Dasgupta, S.1    Hsu, D.2    Monteleoni, C.3
  • 11
    • 71049162986 scopus 로고    scopus 로고
    • Coarse sample complexity bounds for active learning
    • Dasgupta, S. 2005. Coarse sample complexity bounds for active learning. In NIPS.
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    • Dasgupta, S.1
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    • A bound on the label complexity of agnostic active learning
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    • Rates of convergence in active learning
    • Hanneke, S. 2011. Rates of convergence in active learning. The Annals of Statistics 39(1):333-361.
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    • Smooth discrimination analysis
    • Mammen, E.; Tsybakov, A. B.; et al. 1999. Smooth discrimination analysis. The Annals of Statistics 27(6):1808-1829.
    • (1999) The Annals of Statistics , vol.27 , Issue.6 , pp. 1808-1829
    • Mammen, E.1    Tsybakov, A.B.2
  • 19
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    • Algorithmic connections between active learning and stochastic convex optimization
    • Ramdas, A., and Singh, A. 2013a. Algorithmic connections between active learning and stochastic convex optimization. In ALT.
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  • 20
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    • Beyond disagreementbased agnostic active learning
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.