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Volumn 9, Issue , 2010, Pages 916-923

Structured prediction cascades

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX MODEL; COMPUTATIONAL RESOURCES; FILTERED OUTPUT; FILTERING EFFICIENCY; FILTERING ERROR; GENERALIZATION BOUND; HANDWRITING RECOGNITION; HIGHER ORDER; LOSS FUNCTIONS; MARGINALS; MODEL COMPLEXITY; ORDERS OF MAGNITUDE; PART OF SPEECH TAGGING; PREDICTIVE POWER; STRUCTURED PREDICTION;

EID: 84862275042     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (39)

References (17)
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    • Rademacher and Gaussian complexities: Risk bounds and structural results
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  • 4
    • 80053251519 scopus 로고    scopus 로고
    • TAG, dynamic programming, and the perceptron for efficient, feature-rich parsing
    • X. Carreras, M. Collins, and T. Koo. TAG, dynamic programming, and the perceptron for efficient, feature-rich parsing. In Proc. CoNLL, 2008.
    • (2008) Proc. CoNLL
    • Carreras, X.1    Collins, M.2    Koo, T.3
  • 5
    • 85036148712 scopus 로고    scopus 로고
    • A maximum-entropy-inspired parser
    • E. Charniak. A maximum-entropy-inspired parser. In Proc. NAACL, 2000.
    • (2000) Proc. NAACL
    • Charniak, E.1
  • 6
    • 85127836544 scopus 로고    scopus 로고
    • Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms
    • M. Collins. Discriminative training methods for hidden markov models: theory and experiments with perceptron algorithms. In Proc. EMNLP, 2002.
    • (2002) Proc. EMNLP
    • Collins, M.1
  • 9
    • 0142192295 scopus 로고    scopus 로고
    • Conditional random fields: Probabilistic models for segmenting and labeling sequence data
    • J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. ICML, 2001.
    • (2001) Proc. ICML
    • Lafferty, J.1    McCallum, A.2    Pereira, F.3
  • 10
    • 34249852033 scopus 로고
    • Building a large annotated corpus of english: The penn treebank
    • M. Marcus, S. Santorini, and M. Marcinkiewicz. Building a large annotated corpus of english: the penn treebank. Computational Linguistics, 19(2):313-330, 1993.
    • (1993) Computational Linguistics , vol.19 , Issue.2 , pp. 313-330
    • Marcus, M.1    Santorini, S.2    Marcinkiewicz, M.3
  • 11
    • 84862296863 scopus 로고    scopus 로고
    • Sparse forward-backward using minimum divergence beams for fast training of CRFs
    • C. Pal, C. Sutton, and A. McCallum. Sparse forward-backward using minimum divergence beams for fast training of CRFs. In Proc. ICASP, 2006.
    • (2006) Proc. ICASP
    • Pal, C.1    Sutton, C.2    McCallum, A.3
  • 13
    • 85124016637 scopus 로고    scopus 로고
    • A maximum entropy model for part-of-speech tagging
    • A. Ratnaparkhi. A maximum entropy model for part-of-speech tagging. In Proc. EMNLP, 1996.
    • (1996) Proc. EMNLP
    • Ratnaparkhi, A.1
  • 14
    • 48849117633 scopus 로고    scopus 로고
    • Pegasos: Primal estimated sub-gradient SOlver for SVM
    • S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient SOlver for SVM. In Proc. ICML, 2007.
    • (2007) Proc. ICML
    • Shalev-Shwartz, S.1    Singer, Y.2    Srebro, N.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.