메뉴 건너뛰기




Volumn , Issue , 2009, Pages 1193-1200

The uniform hardcore Lemma via approximate Bregman projections

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX OPTIMIZATION; MACHINE LEARNING;

EID: 70349161954     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611973068.129     Document Type: Conference Paper
Times cited : (42)

References (17)
  • 2
    • 85043515682 scopus 로고    scopus 로고
    • Boosting a weak learning algorithm by majority
    • Yoav Freund. Boosting a weak learning algorithm by majority. In Proc. 3rd COLT, 1990.
    • Proc. 3rd COLT, 1990
    • Freund, Y.1
  • 3
    • 0031211090 scopus 로고    scopus 로고
    • Schapire. a decision-theoretic generalization of on-line learning and an application to boosting
    • August
    • Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, August 1997.
    • (1997) Journal of Computer and System Sciences , vol.55 , Issue.1 , pp. 119-139
    • Freund, Y.1    Robert, E.2
  • 4
    • 0042496213 scopus 로고    scopus 로고
    • Tracking the Best Linear Predictor
    • DOI 10.1162/153244301753683726
    • Mark Herbster and Manfred K. Warmuth. Tracking the best linear predictor. Journal of Machine Learning Research, 1:281-309, 2001. (Pubitemid 33687205)
    • (2001) Journal of Machine Learning Research , vol.1 , Issue.4 , pp. 281-309
    • Herbster, M.1    Warmuth, M.K.2
  • 6
    • 70349144746 scopus 로고    scopus 로고
    • Pseudorandom generators from one-way functions: A simple construction for any hardness
    • Thomas Holenstein. Pseudorandom generators from one-way functions: A simple construction for any hardness. In Proc of 3rd TCC. Springer, 2006.
    • Proc of 3rd TCC. Springer, 2006
    • Holenstein, T.1
  • 7
    • 70349123285 scopus 로고    scopus 로고
    • Hard-core distributions for somewhat hard problems
    • Russell Impagliazzo. Hard-core distributions for somewhat hard problems. In Proc 36th IEEE FOCS, 1995.
    • Proc 36th IEEE FOCS, 1995
    • Impagliazzo, R.1
  • 8
    • 38049164495 scopus 로고    scopus 로고
    • Approximately list-decoding direct product codes and uniform hardness amplification
    • Russell Impagliazzo, Ragesh Jaiswal, and Valentine Kabanets. Approximately list-decoding direct product codes and uniform hardness amplification. In Proc. 47th IEEE FOCS, 2006.
    • Proc. 47th IEEE FOCS, 2006
    • Impagliazzo, R.1    Jaiswal, R.2    Kabanets, V.3
  • 11
    • 0038290332 scopus 로고    scopus 로고
    • Boosting and hard-core set construction
    • Adam R. Klivans and Rocco A. Servedio. Boosting and hard-core set construction. Machine Learning, 51(3):217-238, 2003.
    • (2003) Machine Learning , vol.51 , Issue.3 , pp. 217-238
    • Klivans, A.R.1    Servedio, R.A.2
  • 12
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • Robert E. Schapire. The strength of weak learnability. Machine Learning, 5:197-227, 1990.
    • (1990) Machine Learning , vol.5 , pp. 197-227
    • Schapire, R.E.1
  • 13
    • 2542488394 scopus 로고    scopus 로고
    • Smooth boosting and learning with malicious noise
    • Rocco A. Servedio. Smooth boosting and learning with malicious noise. Journal of Machine Learning Research, 4:633-648, 2003.
    • (2003) Journal of Machine Learning Research , vol.4 , pp. 633-648
    • Servedio, R.A.1
  • 16
    • 84864057685 scopus 로고    scopus 로고
    • Randomized pea algorithms with regret bounds that are logarithmic in the dimension
    • Manfred K. Warmuth and Dima Kuzmin. Randomized pea algorithms with regret bounds that are logarithmic in the dimension. In In Proc of NIPS, 2006.
    • In Proc of NIPS, 2006
    • Warmuth, M.K.1    Kuzmin, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.