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Volumn 9, Issue , 2010, Pages 177-184

Neural conditional random fields

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL RANDOM FIELD; GRAPHICAL MODEL; HIGH-LEVEL FEATURES; MARKOV NETWORKS; PROBABILISTIC MODELS; SIGNAL LABELING; STRUCTURED PREDICTION;

EID: 84862291162     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (67)

References (27)
  • 1
    • 85113387701 scopus 로고    scopus 로고
    • Inves- tigating loss functions and optimization methods for discriminative learning of label sequences
    • Altun, Y., Johnson, M., & Hofmann, T. (2003). Inves- tigating loss functions and optimization methods for discriminative learning of label sequences. EMNLP.
    • (2003) EMNLP
    • Altun, Y.1    Johnson, M.2    Hofmann, T.3
  • 3
    • 34547975052 scopus 로고    scopus 로고
    • Scaling learning algorithms towards ai
    • Cambridge, MA: MIT Press
    • Bengio, Y., & LeCun, Y. (2007). Scaling learning algorithms towards ai. In Large scale kernel machines. Cambridge, MA: MIT Press.
    • (2007) Large Scale Kernel Machines
    • Bengio, Y.1    Lecun, Y.2
  • 4
    • 0030648914 scopus 로고    scopus 로고
    • Global training of document processing systems using graph transformer networks
    • Puerto-Rico. IEEE
    • Bottou, L., Bengio, Y., & LeCun, Y. (1997). Global training of document processing systems using graph transformer networks. In Proc. of Computer Vision and Pattern Recognition (pp. 490-494). Puerto-Rico. IEEE.
    • (1997) Proc. of Computer Vision and Pattern Recognition , pp. 490-494
    • Bottou, L.1    Bengio, Y.2    Lecun, Y.3
  • 5
    • 71149085362 scopus 로고    scopus 로고
    • Matrix updates for perceptron training of continuous density hidden markov models
    • Cheng, C.-C., Sha, F., & Saul, L. K. (2009). Matrix updates for perceptron training of continuous density hidden markov models. ICML (pp. 153-160).
    • (2009) ICML , pp. 153-160
    • Cheng, C.-C.1    Sha, F.2    Saul, L.K.3
  • 6
    • 85127836544 scopus 로고    scopus 로고
    • Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms
    • Collins, M. (2002). Discriminative training methods for hidden markov models: theory and experiments with perceptron algorithms. EMNLP (pp. 1-8).
    • (2002) EMNLP , pp. 1-8
    • Collins, M.1
  • 8
    • 71149096870 scopus 로고    scopus 로고
    • Large margin training for hidden Markov models with partially observed states
    • Omnipress
    • Do, T.-M.-T., & Artières, T. (2009). Large margin training for hidden Markov models with partially observed states. ICML (pp. 265-272). Omnipress.
    • (2009) ICML , pp. 265-272
    • Do, T.-M.-T.1    Artières, T.2
  • 9
    • 73249147663 scopus 로고    scopus 로고
    • The difficulty of training deep architectures and the effect of unsupervised pre-training
    • Erhan, D., Manzagol, P.-A., Bengio, Y., Bengio, S., & Vincent, P. (2009). The difficulty of training deep architectures and the effect of unsupervised pre-training. AISTATS.
    • (2009) AISTATS
    • Erhan, D.1    Manzagol, P.-A.2    Bengio, Y.3    Bengio, S.4    Vincent, P.5
  • 10
    • 33749259827 scopus 로고    scopus 로고
    • Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks
    • Graves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006). Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. ICML (pp. 369-376).
    • (2006) ICML , pp. 369-376
    • Graves, A.1    Fernández, S.2    Gomez, F.3    Schmidhuber, J.4
  • 11
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527-1554.
    • (2006) Neural Computation , vol.18 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.-W.3
  • 12
    • 0026982122 scopus 로고
    • Discriminative learning for minimum error classification
    • Juang, B., & Katagiri, S. (1992). Discriminative learning for minimum error classification. IEEE Trans. Signal Processing, Vol.40, No.12.
    • (1992) IEEE Trans. Signal Processing , vol.40 , Issue.12
    • Juang, B.1    Katagiri, S.2
  • 14
    • 0142192295 scopus 로고    scopus 로고
    • Conditional random fields: Probabilistic models for segmenting and labeling sequence data
    • Morgan Kaufmann
    • Lafferty, J. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. ICML (pp. 282-289). Morgan Kaufmann.
    • (2001) ICML , pp. 282-289
    • Lafferty, J.1
  • 15
    • 14344255620 scopus 로고    scopus 로고
    • Kernel conditional random fields: Representation and clique selection
    • Lafferty, J., Zhu, X., & Liu, Y. (2004). Kernel conditional random fields: representation and clique selection. ICML.
    • (2004) ICML
    • Lafferty, J.1    Zhu, X.2    Liu, Y.3
  • 16
    • 0002583871 scopus 로고
    • Speech database development: Design and analysis of the acoustic-phonetic corpus
    • Lamel, L., Kassel, R., & Seneff, S. (1986). Speech database development: Design and analysis of the acoustic-phonetic corpus. DARPA (pp. 100-110).
    • (1986) DARPA , pp. 100-110
    • Lamel, L.1    Kassel, R.2    Seneff, S.3
  • 18
    • 0000747663 scopus 로고    scopus 로고
    • Maximum entropy markov models for information extraction and segmentation
    • Mccallum, A., Freitag, D., & Pereira, F. (2000). Maximum entropy markov models for information extraction and segmentation. ICML (pp. 591-598).
    • (2000) ICML , pp. 591-598
    • McCallum, A.1    Freitag, D.2    Pereira, F.3
  • 19
    • 84863373241 scopus 로고    scopus 로고
    • Conditional neural fields
    • Peng, J., Bo, L., & Xu, J. (2009). Conditional neural fields. NIPS.
    • (2009) NIPS
    • Peng, J.1    Bo, L.2    Xu, J.3
  • 20
    • 84857472364 scopus 로고    scopus 로고
    • Conditional graphical models
    • G. H. Bakir, T. Hofmann, B. Schlkopf, A. J. Smola, B. Taskar and S. V. N. Vishwanathan (Eds.). MIT Press
    • Perez-Cruz, F, G. Z., & Pontil, M. (2007). Conditional graphical models. In G. H. Bakir, T. Hofmann, B. Schlkopf, A. J. Smola, B. Taskar and S. V. N. Vishwanathan (Eds.), Predicting structured data. MIT Press.
    • (2007) Predicting Structured Data
    • Perez-Cruz, F.G.Z.1    Pontil, M.2
  • 22
    • 42549149566 scopus 로고
    • The perceptron: A probabilistic model for information storage and organization in the brain
    • Rosenblatt, F. (1988). The perceptron: a probabilistic model for information storage and organization in the brain. Neurocomputing: foundations of research, 89-114.
    • (1988) Neurocomputing: Foundations of Research , pp. 89-114
    • Rosenblatt, F.1
  • 23
    • 27544451563 scopus 로고    scopus 로고
    • Rna secondary structural alignment with conditional random fields
    • Sato, K., & Sakakibara, Y. (2005). Rna secondary structural alignment with conditional random fields. ECCB/JBI (p. 242).
    • (2005) ECCB/JBI , pp. 242
    • Sato, K.1    Sakakibara, Y.2
  • 24
    • 84864038630 scopus 로고    scopus 로고
    • Large margin hidden markov models for automatic speech recognition
    • MIT Press
    • Sha, F., & Saul, L. K. (2007). Large margin hidden markov models for automatic speech recognition. NIPS 19 (pp. 1249-1256). MIT Press.
    • (2007) NIPS , vol.19 , pp. 1249-1256
    • Sha, F.1    Saul, L.K.2
  • 25
    • 84898948585 scopus 로고    scopus 로고
    • Maxmargin markov networks
    • MIT Press
    • Taskar, B., Guestrin, C., & Koller, D. (2004). Maxmargin markov networks. NIPS 16. MIT Press.
    • (2004) NIPS , vol.16
    • Taskar, B.1    Guestrin, C.2    Koller, D.3
  • 26
    • 84864026688 scopus 로고    scopus 로고
    • Modeling human motion using binary latent variables
    • MIT Press
    • Taylor, G. W., Hinton, G. E., & Roweis, S. T. (2007). Modeling human motion using binary latent variables. In Nips, 1345-1352. MIT Press.
    • (2007) Nips , pp. 1345-1352
    • Taylor, G.W.1    Hinton, G.E.2    Roweis, S.T.3
  • 27
    • 0036461035 scopus 로고    scopus 로고
    • Large scale discriminative training of hidden markov models for speech recognition
    • Woodland, P., & Povey, D. (2002). Large scale discriminative training of hidden markov models for speech recognition. Computer Speech and Language, 16, 25-47(23).
    • (2002) Computer Speech and Language , vol.16 , Issue.23 , pp. 25-47
    • Woodland, P.1    Povey, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.