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Volumn 44, Issue 3, 1997, Pages 427-485

How to use expert advice

Author keywords

Algorithms

Indexed keywords

ARTIFICIAL INTELLIGENCE; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS; PATTERN RECOGNITION;

EID: 0031140246     PISSN: 00045411     EISSN: None     Source Type: Journal    
DOI: 10.1145/258128.258179     Document Type: Article
Times cited : (563)

References (47)
  • 1
    • 21344492381 scopus 로고
    • Rates of convergence for minimum contrast estimators
    • BIRGE, L., AND MASSART, P. 1993. Rates of convergence for minimum contrast estimators. Prob. Theory Rel. Fields 97, 113-150.
    • (1993) Prob. Theory Rel. Fields , vol.97 , pp. 113-150
    • Birge, L.1    Massart, P.2
  • 2
    • 0024750852 scopus 로고
    • Learnability and the Vapnik-Chervonenkis dimension
    • BLUMER, A., EHRENFEUCHT, A., HAUSSLER, D., AND WARMUTH, M. K. 1989. Learnability and the Vapnik-Chervonenkis dimension. J. ACM 36, 4, 929-965.
    • (1989) J. ACM , vol.36 , Issue.4 , pp. 929-965
    • Blumer, A.1    Ehrenfeucht, A.2    Haussler, D.3    Warmuth, M.K.4
  • 5
    • 84860603566 scopus 로고
    • Approximate methods for sequential decision making using expert advice
    • New Brunswick, N.J., July 12-15. ACM, New York
    • CHUNG, T. H. 1994. Approximate methods for sequential decision making using expert advice. In Proceedings of the 7th Annual ACM Conference on Computational Learning Theory (New Brunswick, N.J., July 12-15). ACM, New York, pp. 183-189.
    • (1994) Proceedings of the 7th Annual ACM Conference on Computational Learning Theory , pp. 183-189
    • Chung, T.H.1
  • 7
    • 0017503437 scopus 로고
    • Compound Bayes predictors for sequences with apparent Markov structure
    • June
    • COVER, T. M., AND SHANAR, A. 1977. Compound Bayes predictors for sequences with apparent Markov structure. IEEE Trans. Syst. Man Cybernet. SMC-7, 6 (June), 421-424.
    • (1977) IEEE Trans. Syst. Man Cybernet. , vol.SMC-7 , Issue.6 , pp. 421-424
    • Cover, T.M.1    Shanar, A.2
  • 8
    • 0000582742 scopus 로고
    • Statistical theory: The prequential approach
    • DAWID, A. P. 1984. Statistical theory: The prequential approach. J. Roy. Stat. Soc., Series A, 278-292.
    • (1984) J. Roy. Stat. Soc., Series A , pp. 278-292
    • Dawid, A.P.1
  • 9
    • 0000042224 scopus 로고
    • Prequential analysis, stochastic complexity and Bayesian inference
    • Oxford University Press
    • DAWID, A. 1991. Prequential analysis, stochastic complexity and Bayesian inference. In Bayesian Statistics, vol. 4. Oxford University Press, pp. 109-125.
    • (1991) Bayesian Statistics , vol.4 , pp. 109-125
    • Dawid, A.1
  • 12
    • 84882281845 scopus 로고
    • Universal prediction of individual sequences
    • FEDER, M., MERHAV, N., AND GUTMAN, M. 1992. Universal prediction of individual sequences. IEEE Trans. Inf. Theory 38, 1258-1270.
    • (1992) IEEE Trans. Inf. Theory , vol.38 , pp. 1258-1270
    • Feder, M.1    Merhav, N.2    Gutman, M.3
  • 15
    • 0028460060 scopus 로고
    • Competitive k-server algorithms
    • FIAT, A., RABANI, Y., AND RAVID, Y. 1994. Competitive k-server algorithms. J. Comput Syst. Sci. 48, 3, 410-428.
    • (1994) J. Comput Syst. Sci. , vol.48 , Issue.3 , pp. 410-428
    • Fiat, A.1    Rabani, Y.2    Ravid, Y.3
  • 17
    • 0001976283 scopus 로고
    • Approximation to Bayes risk in repeated play
    • Princeton University Press, Princeton, N.J.
    • HANNAN, J. 1957. Approximation to Bayes risk in repeated play. In Contributions to the Theory of Games, vol. 3. Princeton University Press, Princeton, N.J., pp. 97-139.
    • (1957) Contributions to the Theory of Games , vol.3 , pp. 97-139
    • Hannan, J.1
  • 20
    • 0028132501 scopus 로고
    • Bounds on the sample complexity of Bayesian learning using information theory and the VC dimensions
    • HAUSSLER, D., KEARNS, M., AND SCHAPIRE, R. 1994. Bounds on the sample complexity of Bayesian learning using information theory and the VC dimensions. Mach. Learn. 14, 84-114.
    • (1994) Mach. Learn. , vol.14 , pp. 84-114
    • Haussler, D.1    Kearns, M.2    Schapire, R.3
  • 22
    • 43949159818 scopus 로고
    • Predicting {0, 1}-functions on randomly drawn points
    • HAUSSLER, D., LITTLESTONE, N., AND WARMUTH, M. K. 1994. Predicting {0, 1}-functions on randomly drawn points. Inf. Comput. 115, 2, 248-292.
    • (1994) Inf. Comput. , vol.115 , Issue.2 , pp. 248-292
    • Haussler, D.1    Littlestone, N.2    Warmuth, M.K.3
  • 25
    • 0028460231 scopus 로고
    • Efficient distribution-free learning of probabilistic concepts
    • KEARNS, M. J., AND SCHAPIRE, R. E. 1994. Efficient distribution-free learning of probabilistic concepts. J. Comput. Syst. Sci. 48, 3, 464-497.
    • (1994) J. Comput. Syst. Sci. , vol.48 , Issue.3 , pp. 464-497
    • Kearns, M.J.1    Schapire, R.E.2
  • 27
    • 0002123243 scopus 로고
    • Using experts for predicting continuous outcomes
    • Springer-Verlag, New York
    • KIVINEN, J., AND WARMUTH, M. K. 1994. Using experts for predicting continuous outcomes. In Computational Learning Theory: EuroCOLT '93. Springer-Verlag, New York, pp. 109-120.
    • (1994) Computational Learning Theory: EuroCOLT '93 , pp. 109-120
    • Kivinen, J.1    Warmuth, M.K.2
  • 30
    • 35148838877 scopus 로고
    • The weighted majority algorithm
    • LITTLESTONE, N., AND WARMUTH, M. K. 1994. The weighted majority algorithm. Inf. Comput. 108, 2, 212-261.
    • (1994) Inf. Comput. , vol.108 , Issue.2 , pp. 212-261
    • Littlestone, N.1    Warmuth, M.K.2
  • 31
    • 0027632407 scopus 로고
    • Universal schemes for sequential decision for individual data sequences
    • MERHAV, N., AND FEDER, M. 1993. Universal schemes for sequential decision for individual data sequences. IEEE Trans. Inf. Theory, 39, 4, 1280-1292.
    • (1993) IEEE Trans. Inf. Theory , vol.39 , Issue.4 , pp. 1280-1292
    • Merhav, N.1    Feder, M.2
  • 32
    • 0018015137 scopus 로고
    • Modeling by shortest data description
    • RISSANEN, J. 1978. Modeling by shortest data description. Automatica 14, 465-471.
    • (1978) Automatica , vol.14 , pp. 465-471
    • Rissanen, J.1
  • 33
    • 0000318553 scopus 로고
    • Stochastic complexity and modeling
    • RISSANEN, J. 1986. Stochastic complexity and modeling. Ann. Stat. 14, 3, 1080-1100.
    • (1986) Ann. Stat. , vol.14 , Issue.3 , pp. 1080-1100
    • Rissanen, J.1
  • 34
  • 35
    • 0000755030 scopus 로고
    • Statistical mechanics of learning from examples
    • SEUNG, H. S., SOMPOLINSKY, H., AND TISHBY, N. 1992. Statistical mechanics of learning from examples. Phys. Rev A 45, 8, 6056-6091.
    • (1992) Phys. Rev A , vol.45 , Issue.8 , pp. 6056-6091
    • Seung, H.S.1    Sompolinsky, H.2    Tishby, N.3
  • 36
    • 0001913270 scopus 로고
    • Coding of discrete sources with unknown statistics
    • North-Holland, Amsterdam, The Netherlands
    • SHTARKOV, Yu. M. 1975. Coding of discrete sources with unknown statistics. In Topics in Information Theory. North-Holland, Amsterdam, The Netherlands, pp. 559-574.
    • (1975) Topics in Information Theory , pp. 559-574
    • Shtarkov, Yu.M.1
  • 37
    • 0023380770 scopus 로고
    • Universal sequential coding of single messages
    • July-September
    • SHTARKOV, YU. M. 1987. Universal sequential coding of single messages. Prob. Inf. Transm. 23 (July-September), 175-186.
    • (1987) Prob. Inf. Transm. , vol.23 , pp. 175-186
    • Shtarkov, Yu.M.1
  • 38
    • 0003797476 scopus 로고
    • Learning from examples in large neural networks
    • SOMPOLINSKY, H., TISHBY, N., AND SEUNG, H. S. 1992. Learning from examples in large neural networks. Phys. Rev. Lett. 65, 1683-1686.
    • (1992) Phys. Rev. Lett. , vol.65 , pp. 1683-1686
    • Sompolinsky, H.1    Tishby, N.2    Seung, H.S.3
  • 40
    • 0001957366 scopus 로고
    • Sharper bounds for Gaussian and empirical processes
    • TALAGRAND, M. 1994. Sharper bounds for Gaussian and empirical processes. Ann. Prob. 22, 1, 28-76.
    • (1994) Ann. Prob. , vol.22 , Issue.1 , pp. 28-76
    • Talagrand, M.1
  • 41
    • 0021518106 scopus 로고
    • A theory of the learnable
    • Nov.
    • VALIANT, L. G. 1984. A theory of the learnable. Commun. ACM 27, 11 (Nov.), 1134-1142.
    • (1984) Commun. ACM , vol.27 , Issue.11 , pp. 1134-1142
    • Valiant, L.G.1
  • 43
    • 0040864988 scopus 로고
    • Principles of risk minimization for learning theory
    • John E. Moody, Steve J. Hanson, and Richard P. Lippman, eds. Morgan Kaufmann, San Mateo, Calif.
    • VAPNIK, V. 1992. Principles of risk minimization for learning theory. In Advances in Neural Information Processing Systems, vol. 4. John E. Moody, Steve J. Hanson, and Richard P. Lippman, eds. Morgan Kaufmann, San Mateo, Calif.
    • (1992) Advances in Neural Information Processing Systems , vol.4
    • Vapnik, V.1
  • 45
    • 0026821397 scopus 로고
    • Universal forecasting algorithms
    • Feb.
    • VOVK, V. G. 1992. Universal forecasting algorithms. Inf. Comput. 96, 2 (Feb.), 245-277.
    • (1992) Inf. Comput. , vol.96 , Issue.2 , pp. 245-277
    • Vovk, V.G.1
  • 46
    • 0000591974 scopus 로고
    • A logic of probability, with application to the foundations of statistics
    • VOVK, V. G. 1993. A logic of probability, with application to the foundations of statistics. J. Roy. Statis Soc, Ser. B-Methodolical 55, 2, 317-351.
    • (1993) J. Roy. Statis Soc, Ser. B-Methodolical , vol.55 , Issue.2 , pp. 317-351
    • Vovk, V.G.1
  • 47
    • 0039302673 scopus 로고
    • A loss bound model for on-line stochastic prediction algorithms
    • YAMANISHI, K. 1995. A loss bound model for on-line stochastic prediction algorithms. Inf. Comput. 119, 1, 39-54.
    • (1995) Inf. Comput. , vol.119 , Issue.1 , pp. 39-54
    • Yamanishi, K.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.