메뉴 건너뛰기




Volumn 38, Issue 3, 2012, Pages 479-526

Empirical Risk Minimization for Probabilistic Grammars: Sample Complexity and Hardness of Learning

Author keywords

[No Author keywords available]

Indexed keywords

MAXIMUM PRINCIPLE;

EID: 84865284030     PISSN: 08912017     EISSN: 15309312     Source Type: Journal    
DOI: 10.1162/COLI_a_00092     Document Type: Article
Times cited : (6)

References (56)
  • 1
    • 0005809110 scopus 로고
    • Polynomial learnability of probabilistic concepts with respect to the Kullback-Leiber divergence
    • Abe, N., J. Takeuchi, and M. Warmuth. 1991. Polynomial learnability of probabilistic concepts with respect to the Kullback-Leiber divergence. In Proceedings of the Conference on Learning Theory, pages 277-289.
    • (1991) Proceedings of the Conference On Learning Theory , pp. 277-289
    • Abe, N.1    Takeuchi, J.2    Warmuth, M.3
  • 2
    • 0001841122 scopus 로고
    • On the computational complexity of approximating distributions by probabilistic automata
    • Abe, N. and M. Warmuth. 1992. On the computational complexity of approximating distributions by probabilistic automata. Machine Learning, 2:205-260.
    • (1992) Machine Learning , vol.2 , pp. 205-260
    • Abe, N.1    Warmuth, M.2
  • 3
    • 0023453626 scopus 로고
    • Learning regular sets from queries and counterexamples
    • Angluin, D. 1987. Learning regular sets from queries and counterexamples. Information and Computation, 75:87-106.
    • (1987) Information and Computation , vol.75 , pp. 87-106
    • Angluin, D.1
  • 7
    • 77949875170 scopus 로고    scopus 로고
    • The VC dimension of constraint-based grammars
    • Bane, M., J. Riggle, and M. Sonderegger. 2010. The VC dimension of constraint-based grammars. Lingua, 120(5):1194-1208.
    • (2010) Lingua , vol.120 , Issue.5 , pp. 1194-1208
    • Bane, M.1    Riggle, J.2    Sonderegger, M.3
  • 11
    • 0031321299 scopus 로고    scopus 로고
    • Accurate computation of the relative entropy between stochastic regular grammars
    • Carrasco, R. 1997. Accurate computation of the relative entropy between stochastic regular grammars. Theoretical Informatics and Applications, 31(5):437-444.
    • (1997) Theoretical Informatics and Applications , vol.31 , Issue.5 , pp. 437-444
    • Carrasco, R.1
  • 15
    • 0005921071 scopus 로고    scopus 로고
    • Statistical properties of probabilistic context-free grammars
    • Chi, Z. 1999. Statistical properties of probabilistic context-free grammars. Computational Linguistics, 25(1):131-160.
    • (1999) Computational Linguistics , vol.25 , Issue.1 , pp. 131-160
    • Chi, Z.1
  • 17
    • 84865297336 scopus 로고    scopus 로고
    • Unsupervised learning and grammar induction
    • In Alexander Clark, Chris Fox, and Shalom Lappin, editors, Wiley-Blackwell, London
    • Clark, A. and S. Lappin. 2010. Unsupervised learning and grammar induction. In Alexander Clark, Chris Fox, and Shalom Lappin, editors, The Handbook of Computational Linguistics and Natural Language Processing. Wiley-Blackwell, London, pages 197-220.
    • (2010) The Handbook of Computational Linguistics and Natural Language Processing , pp. 197-220
    • Clark, A.1    Lappin, S.2
  • 18
    • 22944462298 scopus 로고    scopus 로고
    • PAC-learnability of probabilistic deterministic finite state automata
    • Clark, A. and F. Thollard. 2004. PAC-learnability of probabilistic deterministic finite state automata. Journal of Machine Learning Research, 5:473-497.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 473-497
    • Clark, A.1    Thollard, F.2
  • 19
    • 79551472048 scopus 로고    scopus 로고
    • Covariance in unsupervised learning of probabilistic grammars
    • Cohen, S. B. and N. A. Smith. 2010a. Covariance in unsupervised learning of probabilistic grammars. Journal of Machine Learning Research, 11:3017-3051.
    • (2010) Journal of Machine Learning Research , vol.11 , pp. 3017-3051
    • Cohen, S.B.1    Smith, N.A.2
  • 21
    • 84859976571 scopus 로고    scopus 로고
    • Viterbi training for PCFGs: Hardness results and competitiveness of uniform initialization
    • Cohen, S. B. and N. A. Smith. 2010c. Viterbi training for PCFGs: Hardness results and competitiveness of uniform initialization. In Proceedings of the Association for Computational Linguistics, pages 1502-1511.
    • (2010) Proceedings of the Association For Computational Linguistics , pp. 1502-1511
    • Cohen, S.B.1    Smith, N.A.2
  • 22
    • 4043082208 scopus 로고    scopus 로고
    • Head-driven statistical models for natural language processing
    • Collins, M. 2003. Head-driven statistical models for natural language processing. Computational Linguistics, 29:589-637.
    • (2003) Computational Linguistics , vol.29 , pp. 589-637
    • Collins, M.1
  • 23
    • 10044256267 scopus 로고    scopus 로고
    • Parameter estimation for statistical parsing models: Theory and practice of distribution-free methods
    • H. Bunt, J. Carroll, and G. Satta, Kluwer, Dordrecht
    • Collins, M. 2004. Parameter estimation for statistical parsing models: Theory and practice of distribution-free methods. In H. Bunt, J. Carroll, and G. Satta, Text, Speech and Language Technology (New Developments in Parsing Technology). Kluwer, Dordrecht, pages 19-55.
    • (2004) Text, Speech and Language Technology (New Developments In Parsing Technology) , pp. 19-55
    • Collins, M.1
  • 26
    • 0031269467 scopus 로고    scopus 로고
    • The sample complexity of learning fixed-structure bayesian networks
    • Dasgupta, S. 1997. The sample complexity of learning fixed-structure bayesian networks. Machine Learning, 29(2-3):165-180.
    • (1997) Machine Learning , vol.29 , Issue.2-3 , pp. 165-180
    • Dasgupta, S.1
  • 27
    • 19944393257 scopus 로고    scopus 로고
    • A bibliographical study of grammatical inference
    • de la Higuera, C. 2005. A bibliographical study of grammatical inference. Pattern Recognition, 38:1332-1348.
    • (2005) Pattern Recognition , vol.38 , pp. 1332-1348
    • Higuera de la., C.1
  • 32
    • 0002192516 scopus 로고
    • Decision-theoretic generalizations of the PAC model for neural net and other learning applications
    • Haussler, D. 1992. Decision-theoretic generalizations of the PAC model for neural net and other learning applications. Information and Computation, 100:78-150.
    • (1992) Information and Computation , vol.100 , pp. 78-150
    • Haussler, D.1
  • 35
    • 0041633409 scopus 로고    scopus 로고
    • VC-dimensions of finite automata and commutative finite automata with k letters and n states
    • Ishigami, Y. and S. Tani. 1997. VC-dimensions of finite automata and commutative finite automata with k letters and n states. Applied Mathematics, 74(3):229-240.
    • (1997) Applied Mathematics , vol.74 , Issue.3 , pp. 229-240
    • Ishigami, Y.1    Tani, S.2
  • 36
    • 0034198996 scopus 로고    scopus 로고
    • Observable operator models for discrete stochastic time series
    • Jaeger, H. 1999. Observable operator models for discrete stochastic time series. Neural Computation, 12:1371-1398.
    • (1999) Neural Computation , vol.12 , pp. 1371-1398
    • Jaeger, H.1
  • 40
    • 33746194045 scopus 로고    scopus 로고
    • Local Rademacher complexities and oracle inequalities in risk minimization
    • Koltchinskii, V. 2006. Local Rademacher complexities and oracle inequalities in risk minimization. The Annals of Statistics, 34(6):2593-2656.
    • (2006) The Annals of Statistics , vol.34 , Issue.6 , pp. 2593-2656
    • Koltchinskii, V.1
  • 43
    • 0000595627 scopus 로고    scopus 로고
    • Some applications of concentration inequalities to statistics
    • Massart, P. 2000. Some applications of concentration inequalities to statistics. Annales de la Faculté des Sciences de Toulouse, IX(2):245-303.
    • (2000) Annales De La Faculté Des Sciences De Toulouse , vol.9 , Issue.2 , pp. 245-303
    • Massart, P.1
  • 45
    • 84865306063 scopus 로고    scopus 로고
    • PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance
    • Palmer, N. and P. W. Goldberg. 2007. PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance. In Proceedings of Algorithmic Learning Theory, pages 157-170.
    • (2007) Proceedings of Algorithmic Learning Theory , pp. 157-170
    • Palmer, N.1    Goldberg, P.W.2
  • 47
    • 85037549689 scopus 로고
    • Inductive inference, DFAs, and computational complexity
    • Pitt, L. 1989. Inductive inference, DFAs, and computational complexity. Analogical and Inductive Inference, 397:18-44.
    • (1989) Analogical and Inductive Inference , vol.397 , pp. 18-44
    • Pitt, L.1
  • 50
    • 0032049450 scopus 로고    scopus 로고
    • On the learnability and usage of acyclic probabilistic finite automata
    • Ron, D., Y. Singer, and N. Tishby. 1998. On the learnability and usage of acyclic probabilistic finite automata. Journal of Computer and System Sciences, 56(2):133-152.
    • (1998) Journal of Computer and System Sciences , vol.56 , Issue.2 , pp. 133-152
    • Ron, D.1    Singer, Y.2    Tishby, N.3
  • 53
    • 0001957366 scopus 로고
    • Sharper bounds for Gaussian and empirical processes
    • Talagrand, M. 1994. Sharper bounds for Gaussian and empirical processes. Annals of Probability, 22:28-76.
    • (1994) Annals of Probability , vol.22 , pp. 28-76
    • Talagrand, M.1
  • 55
    • 3142725508 scopus 로고    scopus 로고
    • Optimal aggregation of classifiers in statistical learning
    • Tsybakov, A. 2004. Optimal aggregation of classifiers in statistical learning. The Annals of Statistics, 32(1):135-166.
    • (2004) The Annals of Statistics , vol.32 , Issue.1 , pp. 135-166
    • Tsybakov, A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.