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Volumn 1, Issue 1, 2000, Pages 35-44

Finite sample performance guarantees of fusers for function estimators

Author keywords

Function estimation; Multiple model; PAC learning; Sample size estimation

Indexed keywords


EID: 0034216174     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1566-2535(00)00004-X     Document Type: Article
Times cited : (26)

References (46)
  • 6
    • 0030196364 scopus 로고    scopus 로고
    • Stacked regressions
    • L. Brieman, Stacked regressions, Machine Learning 24 (1) (1996) 49-64.
    • (1996) Machine Learning , vol.24 , Issue.1 , pp. 49-64
    • Brieman, L.1
  • 9
    • 84918441630 scopus 로고
    • Geometric and statistical properties of systems of linear inequalities with application in pattern recognition
    • T. Cover, Geometric and statistical properties of systems of linear inequalities with application in pattern recognition, IEEE Trans. on Electronic Comput. EC-14 (1965) 326-334.
    • (1965) IEEE Trans. on Electronic Comput. EC-14 , pp. 326-334
    • Cover, T.1
  • 10
    • 0004197578 scopus 로고
    • IEEE Computer Society Press, Los Alamitos, California
    • B.V. Dasarathy, Decision Fusion, IEEE Computer Society Press, Los Alamitos, California, 1994.
    • (1994) Decision Fusion
    • Dasarathy, B.V.1
  • 12
    • 0002095886 scopus 로고
    • A randomization rule for selecting forecasts
    • D.P. Foster, R.V. Vohra, A randomization rule for selecting forecasts, Operations Research 41 (4) (1993) 704-709.
    • (1993) Operations Research , vol.41 , Issue.4 , pp. 704-709
    • Foster, D.P.1    Vohra, R.V.2
  • 13
    • 58149321460 scopus 로고
    • Boosting a weak learning algorithm by majority
    • Y. Freund, Boosting a weak learning algorithm by majority, Information and Computation 121 (1995) 256-285.
    • (1995) Information and Computation , vol.121 , pp. 256-285
    • Freund, Y.1
  • 15
    • 0029256399 scopus 로고
    • Bounding the Vapnik-Chervonenkis dimension of concept classes parametrized by real numbers
    • P.W. Goldberg, M.R. Jerrum, Bounding the Vapnik-Chervonenkis dimension of concept classes parametrized by real numbers, Machine Learning 18 (1995) 131-148.
    • (1995) Machine Learning , vol.18 , pp. 131-148
    • Goldberg, P.W.1    Jerrum, M.R.2
  • 16
    • 0031171679 scopus 로고    scopus 로고
    • Optimal linear combinations of neural networks
    • S. Hashem, Optimal linear combinations of neural networks, Neural Networks 10 (4) (1997) 599-614.
    • (1997) Neural Networks , vol.10 , Issue.4 , pp. 599-614
    • Hashem, S.1
  • 17
    • 0002192516 scopus 로고
    • Decision theoretic generalizations of the PAC model for neural net and other learning applications
    • D. Haussler, Decision theoretic generalizations of the PAC model for neural net and other learning applications, Information and Computation 100 (1992) 78-150.
    • (1992) Information and Computation , vol.100 , pp. 78-150
    • Haussler, D.1
  • 20
    • 0031077292 scopus 로고    scopus 로고
    • Polynomial bounds for VC dimension of sigmoidal and general Pfaffian neural networks
    • M. Karpinski, A. Macintyre, Polynomial bounds for VC dimension of sigmoidal and general Pfaffian neural networks, J. Comput. and Syst. Sci. 54 (1997) 169-176.
    • (1997) J. Comput. and Syst. Sci. , vol.54 , pp. 169-176
    • Karpinski, M.1    Macintyre, A.2
  • 21
    • 0029251952 scopus 로고
    • Learning from a population of hypotheses
    • M.J. Kearns, H.S. Seung, Learning from a population of hypotheses, Machine Learning 18 (1995) 255-276.
    • (1995) Machine Learning , vol.18 , pp. 255-276
    • Kearns, M.J.1    Seung, H.S.2
  • 23
    • 0030109050 scopus 로고    scopus 로고
    • Nonparametric estimation and classification using radial basis function nets and empirical risk minimization
    • A. Krzyzak, T. Linder, G. Lugosi, Nonparametric estimation and classification using radial basis function nets and empirical risk minimization, IEEE Trans. on Neural Networks 7 (2) (1996) 475-487.
    • (1996) IEEE Trans. on Neural Networks , vol.7 , Issue.2 , pp. 475-487
    • Krzyzak, A.1    Linder, T.2    Lugosi, G.3
  • 26
    • 0004092085 scopus 로고
    • Prentice Hall, Engelwood Cliffs, NJ
    • L. Ljung, System Identification, Prentice Hall, Engelwood Cliffs, NJ, 1987.
    • (1987) System Identification
    • Ljung, L.1
  • 27
    • 0029307575 scopus 로고
    • Nonparametric estimation via empirical risk minimization
    • G. Lugosi, K. Zeger, Nonparametric estimation via empirical risk minimization, IEEE Trans. on Inform. Theory 41 (3) (1995) 677-687.
    • (1995) IEEE Trans. on Inform. Theory , vol.41 , Issue.3 , pp. 677-687
    • Lugosi, G.1    Zeger, K.2
  • 28
    • 0001169492 scopus 로고    scopus 로고
    • Guest editorial on information/decision fusion with engineering applications
    • R.N. Madan, N.S.V. Rao, Guest editorial on information/decision fusion with engineering applications, J. Franklin Inst. 336B (2) (1999) 199-204.
    • (1999) J. Franklin Inst. , vol.336 B , Issue.2 , pp. 199-204
    • Madan, R.N.1    Rao, N.S.V.2
  • 29
    • 0000579826 scopus 로고
    • U-processes: Rates of convergence
    • D. Nolan, D. Pollard, U-processes: Rates of convergence, Ann. Stat. 15 (2) (1987) 780-799.
    • (1987) Ann. Stat. , vol.15 , Issue.2 , pp. 780-799
    • Nolan, D.1    Pollard, D.2
  • 31
    • 84972514302 scopus 로고
    • Asyptotics via empirical processes
    • with discussion
    • D. Pollard, Asyptotics via empirical processes (with discussion), Statistical Science 4 (1989) 341-366.
    • (1989) Statistical Science , vol.4 , pp. 341-366
    • Pollard, D.1
  • 33
    • 0000518496 scopus 로고
    • Fusion methods for multiple sensor systems with unknown error densities
    • N.S.V. Rao, Fusion methods for multiple sensor systems with unknown error densities, J. Franklin Inst. 331B (5) (1994) 509-530.
    • (1994) J. Franklin Inst. , vol.331 B , Issue.5 , pp. 509-530
    • Rao, N.S.V.1
  • 34
    • 0002389879 scopus 로고    scopus 로고
    • Fusion methods in multiple sensor systems using feedforward neural networks
    • N.S.V. Rao, Fusion methods in multiple sensor systems using feedforward neural networks, Intell. Automat. Soft Comput. 5 (1) (1998) 21-30.
    • (1998) Intell. Automat. Soft Comput. , vol.5 , Issue.1 , pp. 21-30
    • Rao, N.S.V.1
  • 35
    • 0033231906 scopus 로고    scopus 로고
    • Simple sample bound for feedforward sigmoid networks with bounded weights
    • N.S.V. Rao, Simple sample bound for feedforward sigmoid networks with bounded weights, Neurocomputing 29 (1999).
    • (1999) Neurocomputing , vol.29
    • Rao, N.S.V.1
  • 37
    • 0032095771 scopus 로고    scopus 로고
    • Function estimation by feedforward sigmoidal networks with bounded weights
    • N.S.V. Rao, V. Protopopescu, Function estimation by feedforward sigmoidal networks with bounded weights, Neural Process. Lett. 7 (1998) 125-131.
    • (1998) Neural Process. Lett. , vol.7 , pp. 125-131
    • Rao, N.S.V.1    Protopopescu, V.2
  • 39
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • R.E. Schapire, The strength of weak learnability, Machine Learning 5 (1990) 197-227.
    • (1990) Machine Learning , vol.5 , pp. 197-227
    • Schapire, R.E.1
  • 40
    • 0021518106 scopus 로고
    • A theory of the learnable
    • L.G. Valiant, A theory of the learnable, Commun. ACM 27 (11) (1984) 1134-1142.
    • (1984) Commun. ACM , vol.27 , Issue.11 , pp. 1134-1142
    • Valiant, L.G.1
  • 44
    • 0003133883 scopus 로고
    • Probabilistic logics and the synthesis of reliable organisms from unreliable components
    • in C.E. Shannon, J. McCarthy (Eds.), Princeton University Press, Princeton
    • J. von Neumann, Probabilistic logics and the synthesis of reliable organisms from unreliable components, in: C.E. Shannon, J. McCarthy (Eds.), Automata Studies, Princeton University Press, Princeton, 1956, pp. 43-98.
    • (1956) Automata Studies , pp. 43-98
    • Von Neumann, J.1


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