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Volumn 2015-January, Issue , 2015, Pages 10-18

Learning with symmetric label noise: The importance of being unhinged

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION SCIENCE;

EID: 84965129272     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (321)

References (27)
  • 1
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    • Learning from noisy examples
    • Dana Angluin and Philip Laird. Learning from noisy examples. Machine Learning, 2(4):343-370, 1988.
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    • Angluin, D.1    Laird, P.2
  • 9
    • 84927972620 scopus 로고    scopus 로고
    • Making risk minimization tolerant to label noise
    • Aritra Ghosh, Naresh Manwani, and P. S. Sastry. Making risk minimization tolerant to label noise. Neurocomputing, 160:93-107, 2015.
    • (2015) Neurocomputing , vol.160 , pp. 93-107
    • Ghosh, A.1    Manwani, N.2    Sastry, P.S.3
  • 10
    • 84925605946 scopus 로고    scopus 로고
    • The entire regularization path for the support vector machine
    • December
    • Trevor Hastie, Saharon Rosset, Robert Tibshirani, and Ji Zhu. The entire regularization path for the support vector machine. Journal of Machine Learning Research, 5:1391-1415, December 2004. ISSN 1532-4435.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 1391-1415
    • Hastie, T.1    Rosset, S.2    Tibshirani, R.3    Zhu, J.4
  • 11
    • 0027188175 scopus 로고    scopus 로고
    • Efficient noise-tolerant learning from statistical queries
    • November
    • Michael Kearns. Efficient noise-tolerant learning from statistical queries. Journal of the ACM, 5(6):392-401, November 1998.
    • (1998) Journal of the ACM , vol.5 , Issue.6 , pp. 392-401
    • Kearns, M.1
  • 12
    • 83555170269 scopus 로고    scopus 로고
    • Random classification noise defeats all convex potential boosters
    • Philip M. Long and Rocco A. Servedio. Random classification noise defeats all convex potential boosters. Machine Learning, 78(3):287-304, 2010. ISSN 0885-6125.
    • (2010) Machine Learning , vol.78 , Issue.3 , pp. 287-304
    • Long, P.M.1    Servedio, R.A.2
  • 14
    • 84890431307 scopus 로고    scopus 로고
    • Noise tolerance under risk minimization
    • June
    • Naresh Manwani and P. S. Sastry. Noise tolerance under risk minimization. IEEE Transactions on Cybernetics, 43(3):1146-1151, June 2013.
    • (2013) IEEE Transactions on Cybernetics , vol.43 , Issue.3 , pp. 1146-1151
    • Manwani, N.1    Sastry, P.S.2
  • 19
    • 79955815221 scopus 로고    scopus 로고
    • Information, divergence and risk for binary experiments
    • Mar.
    • Mark D Reid and Robert C Williamson. Information, divergence and risk for binary experiments. Journal of Machine Learning Research, 12:731-817, Mar. 2011.
    • (2011) Journal of Machine Learning Research , vol.12 , pp. 731-817
    • Reid, M.D.1    Williamson, R.C.2
  • 25
    • 70350610827 scopus 로고    scopus 로고
    • Learning SVMs from sloppily labeled data
    • Springer Berlin Heidelberg
    • Guillaume Stempfel and Liva Ralaivola. Learning SVMs from sloppily labeled data. In Artificial Neural Networks (ICANN), volume 5768, pages 884-893. Springer Berlin Heidelberg, 2009.
    • (2009) Artificial Neural Networks (ICANN) , vol.5768 , pp. 884-893
    • Stempfel, G.1    Ralaivola, L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.