메뉴 건너뛰기




Volumn 227, Issue , 2007, Pages 999-1006

Winnowing subspaces

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; MATRIX ALGEBRA; ONLINE SYSTEMS;

EID: 34547986383     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273622     Document Type: Conference Paper
Times cited : (26)

References (17)
  • 1
    • 35448978697 scopus 로고    scopus 로고
    • Arora, S., & Kale, S. (2007). A combinatorial primaldual approach to semidefinite programs. Proc. 39th Annual ACM Symposium on Theory of Computing. ACM. To appear.
    • Arora, S., & Kale, S. (2007). A combinatorial primaldual approach to semidefinite programs. Proc. 39th Annual ACM Symposium on Theory of Computing. ACM. To appear.
  • 2
    • 0032140937 scopus 로고    scopus 로고
    • Auer, P., & Warmuth, M. K. (1998). Tracking the best disjunction. Machine Learning, 32, 127-150. Earlier version in 36th FOCS, 1995.
    • Auer, P., & Warmuth, M. K. (1998). Tracking the best disjunction. Machine Learning, 32, 127-150. Earlier version in 36th FOCS, 1995.
  • 4
    • 34547994949 scopus 로고    scopus 로고
    • Cesa-Bianchi, N., & Gentile, C. (2006). Improved risk tail bounds for on-line learning. Advances in Neural Information Processing Systems 18 (NIPS 05). MIT Press.
    • Cesa-Bianchi, N., & Gentile, C. (2006). Improved risk tail bounds for on-line learning. Advances in Neural Information Processing Systems 18 (NIPS 05). MIT Press.
  • 5
    • 0029521676 scopus 로고
    • Sample compression, learnability, and the Vapnik-Chervonenkis dimension
    • Floyd, S., & Warmuth, M. (1995). Sample compression, learnability, and the Vapnik-Chervonenkis dimension. Machine Learning, 21, 269-304.
    • (1995) Machine Learning , vol.21 , pp. 269-304
    • Floyd, S.1    Warmuth, M.2
  • 6
    • 34547967421 scopus 로고    scopus 로고
    • Gentile, C., & Warmuth, M. K. (1998). Hinge loss and average margin. In Advances in Neural Information Processing Systems 11 (NIPS*98).
    • Gentile, C., & Warmuth, M. K. (1998). Hinge loss and average margin. In Advances in Neural Information Processing Systems 11 (NIPS*98).
  • 7
    • 0004236492 scopus 로고    scopus 로고
    • third edition, The John Hopkins University Press
    • Golub, G. H., & Loan, C. F. V. (1996). Matrix computations (third edition). The John Hopkins University Press.
    • (1996) Matrix computations
    • Golub, G.H.1    Loan, C.F.V.2
  • 9
    • 0008815681 scopus 로고    scopus 로고
    • Additive versus exponentiated gradient updates for linear prediction
    • Kivinen, J., & Warmuth, M. K. (1997). Additive versus exponentiated gradient updates for linear prediction. Information and Computation, 132, 1-64.
    • (1997) Information and Computation , vol.132 , pp. 1-64
    • Kivinen, J.1    Warmuth, M.K.2
  • 11
    • 34250091945 scopus 로고
    • Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
    • Littlestone, N. (1988). Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2, 285-318.
    • (1988) Machine Learning , vol.2 , pp. 285-318
    • Littlestone, N.1
  • 14
    • 21844471282 scopus 로고    scopus 로고
    • Matrix exponentiated gradient updates for on-line learning and Bregman projections
    • Tsuda, K., Rätsch, G., & Warmuth, M. K. (2005). Matrix exponentiated gradient updates for on-line learning and Bregman projections. Journal of Machine Learning Research, 6, 995-1018.
    • (2005) Journal of Machine Learning Research , vol.6 , pp. 995-1018
    • Tsuda, K.1    Rätsch, G.2    Warmuth, M.K.3
  • 16
    • 34547981848 scopus 로고    scopus 로고
    • Warmuth, M. K., & Kuzmin, D. (2006a). Online variance minimization. Proceedings of the 19th Annual Conference on Learning Theory (COLT 06). Pittsburg: Springer.
    • Warmuth, M. K., & Kuzmin, D. (2006a). Online variance minimization. Proceedings of the 19th Annual Conference on Learning Theory (COLT 06). Pittsburg: Springer.
  • 17
    • 34547998520 scopus 로고    scopus 로고
    • Warmuth, M. K., & Kuzmin, D. (2006b). Randomized PCA algorithms with regret bounds that are logarithmic in the dimension. Advances in Neural Information Processing Systems 19 (NIPS 06). MIT Press.
    • Warmuth, M. K., & Kuzmin, D. (2006b). Randomized PCA algorithms with regret bounds that are logarithmic in the dimension. Advances in Neural Information Processing Systems 19 (NIPS 06). MIT Press.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.