메뉴 건너뛰기




Volumn , Issue , 2011, Pages

Lower bounds for passive and active learning

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; INFORMATION THEORY;

EID: 85162323750     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (36)

References (27)
  • 1
    • 0043124612 scopus 로고
    • Rates of growth and sample moduli for weighted empirical processes indexed by sets
    • K.S. Alexander. Rates of growth and sample moduli for weighted empirical processes indexed by sets. Probability Theory and Related Fields, 75(3):379-423, 1987.
    • (1987) Probability Theory and Related Fields , vol.75 , Issue.3 , pp. 379-423
    • Alexander, K.S.1
  • 2
    • 0000227616 scopus 로고
    • The central limit theorem for weighted empirical processes indexed by sets
    • K.S. Alexander. The central limit theorem for weighted empirical processes indexed by sets. Journal of Multivariate Analysis, 22(2):313-339, 1987.
    • (1987) Journal of Multivariate Analysis , vol.22 , Issue.2 , pp. 313-339
    • Alexander, K.S.1
  • 3
    • 0001199215 scopus 로고
    • A general class of coefficients of divergence of one distribution from another
    • S. M. Ali and S. D. Silvey. A general class of coefficients of divergence of one distribution from another. J. Roy. Stat. Soc. Ser. B, 28:131-142, 1966.
    • (1966) J. Roy. Stat. Soc. Ser. B , vol.28 , pp. 131-142
    • Ali, S.M.1    Silvey, S.D.2
  • 5
    • 70049107426 scopus 로고    scopus 로고
    • Importance weighted active learning
    • ACM New York, NY, USA
    • A. Beygelzimer, S. Dasgupta, and J. Langford. Importance weighted active learning. In ICML. ACM New York, NY, USA, 2009.
    • (2009) ICML
    • Beygelzimer, A.1    Dasgupta, S.2    Langford, J.3
  • 6
    • 80052365883 scopus 로고
    • An interval estimation problem for controlled observations
    • M.V. Burnashev and K.S. Zigangirov. An interval estimation problem for controlled observations. Problemy Peredachi Informatsii, 10(3):51-61, 1974.
    • (1974) Problemy Peredachi Informatsii , vol.10 , Issue.3 , pp. 51-61
    • Burnashev, M.V.1    Zigangirov, K.S.2
  • 7
    • 43749112531 scopus 로고    scopus 로고
    • Minimax bounds for active learning
    • R. M. Castro and R. D. Nowak. Minimax bounds for active learning. IEEE Trans. Inform. Theory, 54(5):2339-2353, 2008.
    • (2008) IEEE Trans. Inform. Theory , vol.54 , Issue.5 , pp. 2339-2353
    • Castro, R.M.1    Nowak, R.D.2
  • 9
    • 0028424239 scopus 로고
    • Improving generalization with active learning
    • D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, 15(2):201-221, 1994.
    • (1994) Machine Learning , vol.15 , Issue.2 , pp. 201-221
    • Cohn, D.1    Atlas, L.2    Ladner, R.3
  • 10
    • 0000489740 scopus 로고
    • Information-type measures of difference of probability distributions and indirect observations
    • I. Csiszár. Information-type measures of difference of probability distributions and indirect observations. Studia Sci. Math. Hungar., 2:299-318, 1967.
    • (1967) Studia Sci. Math. Hungar. , vol.2 , pp. 299-318
    • Csiszár, I.1
  • 13
    • 0031209604 scopus 로고    scopus 로고
    • Selective sampling using the query by committee algorithm
    • Y. Freund, H.S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28(2):133-168, 1997.
    • (1997) Machine Learning , vol.28 , Issue.2 , pp. 133-168
    • Freund, Y.1    Seung, H.S.2    Shamir, E.3    Tishby, N.4
  • 14
    • 0035354041 scopus 로고    scopus 로고
    • Improved lower bounds for learning from noisy examples: An informationtheoretic approach
    • C. Gentile and D. P. Helmbold. Improved lower bounds for learning from noisy examples: an informationtheoretic approach. Inform. Comput., 166:133-155, 2001.
    • (2001) Inform. Comput. , vol.166 , pp. 133-155
    • Gentile, C.1    Helmbold, D.P.2
  • 15
    • 33746227538 scopus 로고    scopus 로고
    • Concentration inequalities and asymptotic results for ratio type empirical processes
    • E. Giné and V. Koltchinskii. Concentration inequalities and asymptotic results for ratio type empirical processes. Ann. Statist., 34(3):1143-1216, 2006.
    • (2006) Ann. Statist. , vol.34 , Issue.3 , pp. 1143-1216
    • Giné, E.1    Koltchinskii, V.2
  • 16
    • 79952844965 scopus 로고    scopus 로고
    • Lower bounds for the minimax risk using f-divergences and applications
    • A. Guntuboyina. Lower bounds for the minimax risk using f-divergences, and applications. IEEE Trans. Inf. Theory, 57(4):2386-2399, 2011.
    • (2011) IEEE Trans. Inf. Theory , vol.57 , Issue.4 , pp. 2386-2399
    • Guntuboyina, A.1
  • 18
    • 0028460334 scopus 로고
    • Generalizing the Fano inequality
    • T. S. Han and S. Verdú. Generalizing the Fano inequality. IEEE Trans. Inf. Theory, 40(4):1247-1251, 1994.
    • (1994) IEEE Trans. Inf. Theory , vol.40 , Issue.4 , pp. 1247-1251
    • Han, T.S.1    Verdú, S.2
  • 20
    • 79551594780 scopus 로고    scopus 로고
    • Rates of convergence in active learning
    • S. Hanneke. Rates of convergence in active learning. Ann. Statist., 39(1):333-361, 2011.
    • (2011) Ann. Statist. , vol.39 , Issue.1 , pp. 333-361
    • Hanneke, S.1
  • 21
    • 84938606227 scopus 로고
    • Generalized teaching dimensions and the query complexity of learning
    • New York, NY, USA, ACM
    • T. Hegedüs. Generalized teaching dimensions and the query complexity of learning. In COLT '95, pages 108-117, New York, NY, USA, 1995. ACM.
    • (1995) COLT '95 , pp. 108-117
    • Hegedüs, T.1
  • 22
    • 33750727664 scopus 로고    scopus 로고
    • Active learning in the non-realizable case
    • M. Kääriäinen. Active learning in the non-realizable case. In ALT, pages 63-77, 2006.
    • (2006) ALT , pp. 63-77
    • Kääriäinen, M.1
  • 23
    • 78649426154 scopus 로고    scopus 로고
    • Rademacher complexities and bounding the excess risk of active learning
    • V. Koltchinskii. Rademacher complexities and bounding the excess risk of active learning. J. Machine Learn. Res., 11:2457-2485, 2010.
    • (2010) J. Machine Learn. Res. , vol.11 , pp. 2457-2485
    • Koltchinskii, V.1
  • 24
    • 33746243474 scopus 로고    scopus 로고
    • Risk bounds for statistical learning
    • P. Massart and É. Nédélec. Risk bounds for statistical learning. Ann. Statist., 34(5):2326-2366, 2006.
    • (2006) Ann. Statist. , vol.34 , Issue.5 , pp. 2326-2366
    • Massart, P.1    Nédélec, E.2
  • 27
    • 0033233737 scopus 로고    scopus 로고
    • Information-theoretic determination of minimax rates of convergence
    • Y. Yang and A. Barron. Information-theoretic determination of minimax rates of convergence. Ann. Statist., 27(5):1564-1599, 1999.
    • (1999) Ann. Statist. , vol.27 , Issue.5 , pp. 1564-1599
    • Yang, Y.1    Barron, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.