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Volumn , Issue , 2003, Pages 9-16

An SVM Based Voting Algorithm with Application to Parse Reranking

Author keywords

[No Author keywords available]

Indexed keywords

RE-RANKING; RECALL AND PRECISION; SEQUENTIAL MODELING; SUPPORT VECTORS MACHINE; TREEBANKS; VOTING ALGORITHM;

EID: 85123782758     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (48)

References (26)
  • 5
    • 33646057547 scopus 로고    scopus 로고
    • New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron
    • Michael Collins and Nigel Duffy. 2002. New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In Proceedings of ACL 2002.
    • (2002) Proceedings of ACL 2002
    • Collins, Michael1    Duffy, Nigel2
  • 10
    • 0033281425 scopus 로고    scopus 로고
    • Large margin classification using the perceptron algorithm
    • Yoav Freund and Robert E. Schapire. 1999. Large margin classification using the perceptron algorithm. Machine Learning, 37(3):277–296.
    • (1999) Machine Learning , vol.37 , Issue.3 , pp. 277-296
    • Freund, Yoav1    Schapire, Robert E.2
  • 11
    • 84868111801 scopus 로고    scopus 로고
    • A new approximate maximal margin classification algorithm
    • Claudio Gentile. 2001. A new approximate maximal margin classification algorithm. Journal of Machine Learning Research, 2:213–242.
    • (2001) Journal of Machine Learning Research , vol.2 , pp. 213-242
    • Gentile, Claudio1
  • 15
    • 0002709670 scopus 로고    scopus 로고
    • Tree-adjoining grammars
    • G. Rozenberg and A. Salomaa, editors, pages Springer
    • A. Joshi and Y. Schabes. 1997. Tree-adjoining grammars. In G. Rozenberg and A. Salomaa, editors, Handbook of Formal Languages, volume 3, pages 69 – 124. Springer.
    • (1997) Handbook of Formal Languages , vol.3 , pp. 69-124
    • Joshi, A.1    Schabes, Y.2
  • 16
    • 36149029495 scopus 로고
    • Learning algorithms with optimal stability in neural networks
    • W. Krauth and M. Mezard. 1987. Learning algorithms with optimal stability in neural networks. Journal of Physics A, 20:745–752.
    • (1987) Journal of Physics A , vol.20 , pp. 745-752
    • Krauth, W.1    Mezard, M.2
  • 18
    • 0142192295 scopus 로고    scopus 로고
    • Conditional random fields: Probabilistic models for segmentation and labeling sequence data
    • J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmentation and labeling sequence data. In Proceedings of ICML.
    • (2001) Proceedings of ICML
    • Lafferty, J.1    McCallum, A.2    Pereira, F.3
  • 21
    • 0003243224 scopus 로고    scopus 로고
    • Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
    • MIT Press
    • John Platt. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers. MIT Press.
    • (1999) Advances in Large Margin Classifiers
    • Platt, John1
  • 24
    • 4244157268 scopus 로고    scopus 로고
    • Introduction to large margin classifiers
    • A.J. Smola, P. Bartlett, B. Schölkopf, and C. Schuurmans, editors, pages MIT Press
    • A.J. Smola, P. Bartlett, B. Schölkopf, and C. Schuurmans. 2000. Introduction to large margin classifiers. In A.J. Smola, P. Bartlett, B. Schölkopf, and C. Schuurmans, editors, Advances in Large Margin Classifiers, pages 1–26. MIT Press.
    • (2000) Advances in Large Margin Classifiers , pp. 1-26
    • Smola, A.J.1    Bartlett, P.2    Schölkopf, B.3    Schuurmans, C.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.