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Volumn 227, Issue , 2007, Pages 863-870

Piecewise pseudolikelihood for efficient training of conditional random fields

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMPUTATIONAL EFFICIENCY; GRAPHIC METHODS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD; PIECEWISE LINEAR TECHNIQUES;

EID: 34547987363     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273605     Document Type: Conference Paper
Times cited : (55)

References (22)
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    • Besag, J.1
  • 3
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    • Learning as search optimization: Approximate large margin methods for structured prediction
    • Bonn, Germany
    • Daumé III, H., & Marcu, D. (2005). Learning as search optimization: Approximate large margin methods for structured prediction. International Conference on Machine Learning (ICML). Bonn, Germany.
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  • 6
    • 0009856498 scopus 로고
    • Consistency of maximum likelihood and pseudolikelihood estimators for gibbs distributions
    • W. Fleming and P. Lions Eds, New York: Springer
    • Gidas, B. (1988). Consistency of maximum likelihood and pseudolikelihood estimators for gibbs distributions. In W. Fleming and P. Lions (Eds.), Stochastic differential systems, stochastic control theory and applications. New York: Springer.
    • (1988) Stochastic differential systems, stochastic control theory and applications
    • Gidas, B.1
  • 7
    • 33749425692 scopus 로고    scopus 로고
    • Consistency of pseudolikelihood estimation of fully visible boltzmann machines
    • Hyvarinen, A. (2006). Consistency of pseudolikelihood estimation of fully visible boltzmann machines. Neural Computation (pp. 2283-92).
    • (2006) Neural Computation , pp. 2283-2292
    • Hyvarinen, A.1
  • 9
    • 0000747663 scopus 로고    scopus 로고
    • Maximum entropy Markov models for information extraction and segmentation
    • Morgan Kaufmann, San Francisco, CA
    • McCallum, A., Freitag, D., & Pereira, F. (2000). Maximum entropy Markov models for information extraction and segmentation. Proc. 17th International Conf. on Machine Learning (pp. 591-598). Morgan Kaufmann, San Francisco, CA.
    • (2000) Proc. 17th International Conf. on Machine Learning , pp. 591-598
    • McCallum, A.1    Freitag, D.2    Pereira, F.3
  • 10
    • 85121365374 scopus 로고    scopus 로고
    • Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons
    • McCallum, A., & Li, W. (2003). Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. Seventh Conference on Natural Language Learning (CoNLL).
    • (2003) Seventh Conference on Natural Language Learning (CoNLL)
    • McCallum, A.1    Li, W.2
  • 18
    • 33750032384 scopus 로고    scopus 로고
    • An introduction to conditional random fields for relational learning
    • L. Getoor and B. Taskar Eds, MIT Press. To appear
    • Sutton, C., & McCallum, A. (2006). An introduction to conditional random fields for relational learning. In L. Getoor and B. Taskar (Eds.), Introduction to statistical relational learning. MIT Press. To appear.
    • (2006) Introduction to statistical relational learning
    • Sutton, C.1    McCallum, A.2
  • 19
    • 34547991666 scopus 로고    scopus 로고
    • Local training and belief propagation
    • TR-2006-121, Microsoft Research
    • Sutton, C., &, Minka, T. (2006). Local training and belief propagation (Technical Report TR-2006-121). Microsoft Research.
    • (2006) Technical Report
    • Sutton, C.1    Minka, T.2
  • 21
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    • Feature-rich part-of-speech tagging with a cyclic dependency network
    • Toutanova, K., Klein, D., Manning, C. D., & Singer, Y. (2003). Feature-rich part-of-speech tagging with a cyclic dependency network. HLT-NAACL.
    • (2003) HLT-NAACL
    • Toutanova, K.1    Klein, D.2    Manning, C.D.3    Singer, Y.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.