메뉴 건너뛰기




Volumn , Issue , 2002, Pages

Convolution kernels for natural language

Author keywords

[No Author keywords available]

Indexed keywords

FORESTRY; TREES (MATHEMATICS);

EID: 84898995383     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (225)

References (16)
  • 1
    • 0000874557 scopus 로고
    • Theoretical foundations of the potential Function method in pattern recognition learning
    • Aizerman, M., Braverman, E., and Rozonoer, L. (1964). Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning. Automation and Remote Control, 25:821-837.
    • (1964) Automation and Remote Control , vol.25 , pp. 821-837
    • Aizerman, M.1    Braverman, E.2    Rozonoer, L.3
  • 3
    • 0031343557 scopus 로고    scopus 로고
    • Statistical techniques for natural language parsing
    • Charniak, E. (1997). Statistical techniques for natural language parsing. In AI Magazine, Vol. 18, No. 4.
    • (1997) AI Magazine , vol.18 , Issue.4
    • Charniak, E.1
  • 5
    • 33747480560 scopus 로고    scopus 로고
    • Parsing with a single neuron: Convolution kernels for natural language problems
    • University of California at Santa Cruz
    • Collins, M. and Duffy, N. (2001). Parsing with a Single Neuron: Convolution Kernels for Natural Language Problems. Technical report UCSC-CRL-01-01, University of California at Santa Cruz.
    • (2001) Technical Report UCSC-CRL-01-01
    • Collins, M.1    Duffy, N.2
  • 6
    • 34249753618 scopus 로고
    • Support-vector networks
    • Cortes, C. and Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3):273-297.
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 7
    • 0033281425 scopus 로고    scopus 로고
    • Large margin classification using the perceptron algorithm
    • Freund, Y. and Schapire, R. (1999). Large Margin Classification using the Perceptron Algorithm. In Machine Learning, 37(3):277-296.
    • (1999) Machine Learning , vol.37 , Issue.3 , pp. 277-296
    • Freund, Y.1    Schapire, R.2
  • 11
    • 84898986883 scopus 로고    scopus 로고
    • The DOP estimation method is biased and inconsistent
    • To appear
    • Johnson, M. The DOP estimation method is biased and inconsistent. To appear in Computational Linguistics.
    • Computational Linguistics
    • Johnson, M.1
  • 14
    • 34249852033 scopus 로고
    • Building a large annotated corpus of english: The penn treebank
    • Marcus, M., Santorini, B., & Marcinkiewicz, M. (1993). Building a large annotated corpus of english: The Penn treebank. Computational Linguistics, 19, 313-330.
    • (1993) Computational Linguistics , vol.19 , pp. 313-330
    • Marcus, M.1    Santorini, B.2    Marcinkiewicz, M.3
  • 15
    • 0002570938 scopus 로고    scopus 로고
    • Kernel principal component analysis
    • B. Scholkopf, C. J. C. Burges, and A. J. Smola, editors, MIT Press, Cambridge, MA
    • Scholkopf, B., Smola, A., and Muller, K.-R. (1999). Kernel principal component analysis. In B. Scholkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - SV Learning, pages 327-352. MIT Press, Cambridge, MA.
    • (1999) Advances in Kernel Methods - SV Learning , pp. 327-352
    • Scholkopf, B.1    Smola, A.2    Muller, K.-R.3
  • 16
    • 0002531715 scopus 로고    scopus 로고
    • Dynamic alignment kernels
    • A.J. Smola, P.L. Bartlett, B. Schlkopf, and D. Schuurmans, editors, MIT Press
    • Watkins, C. (2000). Dynamic alignment kernels. In A.J. Smola, P.L. Bartlett, B. Schlkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 39-50, MIT Press.
    • (2000) Advances in Large Margin Classifiers , pp. 39-50
    • Watkins, C.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.