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Volumn 42, Issue 11, 2009, Pages 2637-2648

Component-based discriminative classification for hidden Markov models

Author keywords

Dimensionality reduction; Discriminative classification; Generative embeddings; Hidden Markov models; Hybrid models

Indexed keywords

COMPONENT BASED; DIMENSIONALITY REDUCTION; DISCRIMINATIVE CLASSIFICATION; DISCRIMINATIVE CLASSIFIERS; FISHER KERNELS; GENERATIVE EMBEDDINGS; HYBRID MODELS; MAXIMUM A POSTERIORS; PRIOR KNOWLEDGE; SEQUENCE MODELING; SPECIFIC COMPONENT; TEST SEQUENCE; TRAINING DATA; TRAINING FEATURES; VECTOR SPACES;

EID: 67649388040     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.03.023     Document Type: Article
Times cited : (27)

References (64)
  • 1
    • 0024610919 scopus 로고
    • A tutorial on hidden Markov models and selected applications in speech recognition
    • Rabiner L. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77 2 (1989) 257-286
    • (1989) Proceedings of the IEEE , vol.77 , Issue.2 , pp. 257-286
    • Rabiner, L.1
  • 10
    • 0002634158 scopus 로고    scopus 로고
    • Multiscale hidden Markov models for Bayesian image analysis
    • B. Vidakovic, P. Muller Eds, Bayesian Inference in Wavelet Based Models, Springer, Berlin
    • R. Nowak, Multiscale hidden Markov models for Bayesian image analysis, in: B. Vidakovic, P. Muller (Eds.), Bayesian Inference in Wavelet Based Models, Lecture Notes in Statistics, vol. 141, Springer, Berlin, 1999.
    • (1999) Lecture Notes in Statistics , vol.141
    • Nowak, R.1
  • 11
    • 0028181441 scopus 로고
    • Hidden Markov models in computational biology: applications to protein modeling
    • Krogh A., Brown M., Mian I., Sjolander K., and Haussler D. Hidden Markov models in computational biology: applications to protein modeling. Journal of Molecular Biology 235 (1994) 1501-1531
    • (1994) Journal of Molecular Biology , vol.235 , pp. 1501-1531
    • Krogh, A.1    Brown, M.2    Mian, I.3    Sjolander, K.4    Haussler, D.5
  • 13
    • 0035798406 scopus 로고    scopus 로고
    • Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure
    • Gough J., Karplus K., Hughey R., and Chothia C. Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. Journal of Molecular Biology 313 (2001) 903-919
    • (2001) Journal of Molecular Biology , vol.313 , pp. 903-919
    • Gough, J.1    Karplus, K.2    Hughey, R.3    Chothia, C.4
  • 14
    • 84965063004 scopus 로고
    • An inequality with applications to statistical estimation for probabilistic functions of a Markov process and to a model for ecology
    • Baum L., and Egon J. An inequality with applications to statistical estimation for probabilistic functions of a Markov process and to a model for ecology. Bulletin of the American Meteorology Society 73 (1967) 360-363
    • (1967) Bulletin of the American Meteorology Society , vol.73 , pp. 360-363
    • Baum, L.1    Egon, J.2
  • 15
    • 0001862769 scopus 로고
    • An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes
    • Baum L. An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes. Inequality 3 (1970) 1-8
    • (1970) Inequality , vol.3 , pp. 1-8
    • Baum, L.1
  • 17
    • 0032119668 scopus 로고    scopus 로고
    • The hierarchical hidden Markov model: analysis and applications
    • Fine S., Singer Y., and Tishby N. The hierarchical hidden Markov model: analysis and applications. Machine Learning 32 (1998) 41-62
    • (1998) Machine Learning , vol.32 , pp. 41-62
    • Fine, S.1    Singer, Y.2    Tishby, N.3
  • 18
    • 0035118717 scopus 로고    scopus 로고
    • Experiments on the application of IOHMMs to model financial returns series
    • Bengio Y., Lauzon V.-P., and Ducharme R. Experiments on the application of IOHMMs to model financial returns series. IEEE Transactions on Neural Networks 12 1 (2001) 113-123
    • (2001) IEEE Transactions on Neural Networks , vol.12 , Issue.1 , pp. 113-123
    • Bengio, Y.1    Lauzon, V.-P.2    Ducharme, R.3
  • 19
    • 0031268341 scopus 로고    scopus 로고
    • Factorial hidden Markov models
    • Ghahramani Z., and Jordan M. Factorial hidden Markov models. Machine Learning 29 (1997) 245-273
    • (1997) Machine Learning , vol.29 , pp. 245-273
    • Ghahramani, Z.1    Jordan, M.2
  • 22
    • 84871614543 scopus 로고    scopus 로고
    • A novel loss function for the overall risk criterion based discriminative training of HMM models
    • Beijing, China
    • Z. Kaiser, B. Horvat, Z. Kacic, A novel loss function for the overall risk criterion based discriminative training of HMM models, in: International Conference on Spoken Language Processing, vol. 2, Beijing, China, 2000, pp. 887-890.
    • (2000) International Conference on Spoken Language Processing , vol.2 , pp. 887-890
    • Kaiser, Z.1    Horvat, B.2    Kacic, Z.3
  • 23
    • 0010606022 scopus 로고
    • Discriminative training of hidden Markov models using overall risk criterion and reduced gradient method
    • Madrid, Spain
    • K. Na, B. Jeon, D. Chang, S. Chae, S. Ann, Discriminative training of hidden Markov models using overall risk criterion and reduced gradient method, in: European Conference on Speech Communication and Technology, Madrid, Spain, 1995, pp. 97-100.
    • (1995) European Conference on Speech Communication and Technology , pp. 97-100
    • Na, K.1    Jeon, B.2    Chang, D.3    Chae, S.4    Ann, S.5
  • 24
    • 0036836844 scopus 로고    scopus 로고
    • Overall risk criterion estimation of hidden Markov model parameters
    • Kaiser Z., Horvat B., and Kacic Z. Overall risk criterion estimation of hidden Markov model parameters. Speech Communication 38 3-4 (2002) 383-398
    • (2002) Speech Communication , vol.38 , Issue.3-4 , pp. 383-398
    • Kaiser, Z.1    Horvat, B.2    Kacic, Z.3
  • 25
    • 0036461035 scopus 로고    scopus 로고
    • Large scale discriminative training of hidden Markov models for speech recognition
    • Woodland P.C., and Povey D. Large scale discriminative training of hidden Markov models for speech recognition. Computer Speech and Language 16 (2002) 25-47
    • (2002) Computer Speech and Language , vol.16 , pp. 25-47
    • Woodland, P.C.1    Povey, D.2
  • 27
    • 0142192295 scopus 로고    scopus 로고
    • Conditional random fields: Probabilistic models for segmenting and labelling sequence data
    • J. Lafferty, A. McCallum, F. Pereira, Conditional random fields: probabilistic models for segmenting and labelling sequence data, in: International Conference on Machine Learning, 2001, pp. 591-598.
    • (2001) International Conference on Machine Learning , pp. 591-598
    • Lafferty, J.1    McCallum, A.2    Pereira, F.3
  • 28
    • 33745185781 scopus 로고    scopus 로고
    • Hidden conditional random fields for phone classification
    • Lisbon, Portugal
    • A. Gunawardana, M. Mahajan, A. Acero, J. Platt, Hidden conditional random fields for phone classification, in: Interspeech, Lisbon, Portugal, 2005, pp. 1117-1120.
    • (2005) Interspeech , pp. 1117-1120
    • Gunawardana, A.1    Mahajan, M.2    Acero, A.3    Platt, J.4
  • 30
    • 85127836544 scopus 로고    scopus 로고
    • Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithm
    • M. Collins, Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithm, in: Conference on Empirical Methods in Natural Language Processing, 2002.
    • (2002) Conference on Empirical Methods in Natural Language Processing
    • Collins, M.1
  • 32
    • 0026692226 scopus 로고
    • Stacked generalization
    • Wolpert D. Stacked generalization. Neural Networks 5 2 (1992) 241-260
    • (1992) Neural Networks , vol.5 , Issue.2 , pp. 241-260
    • Wolpert, D.1
  • 34
    • 33751584745 scopus 로고    scopus 로고
    • The combining classifier: To train or not to train?
    • Canada
    • R. Duin, The combining classifier: to train or not to train? in: International Conference on Pattern Recognition, vol. II, Canada, 2002, pp. 765-770.
    • (2002) International Conference on Pattern Recognition , vol.2 , pp. 765-770
    • Duin, R.1
  • 35
    • 12344268029 scopus 로고    scopus 로고
    • Similarity-based classification of sequences using hidden Markov models
    • Bicego M., Murino V., and Figueiredo M. Similarity-based classification of sequences using hidden Markov models. Pattern Recognition 37 12 (2004) 2281-2291
    • (2004) Pattern Recognition , vol.37 , Issue.12 , pp. 2281-2291
    • Bicego, M.1    Murino, V.2    Figueiredo, M.3
  • 36
    • 37249001271 scopus 로고    scopus 로고
    • Group-induced vector spaces
    • M. Haindl, J. Kittler, F. Roli Eds, Multiple Classifier Systems, Springer, Berlin
    • M. Bicego, E. Pȩkalska, R. Duin, Group-induced vector spaces, in: M. Haindl, J. Kittler, F. Roli (Eds.), Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 4472, Springer, Berlin, 2007, pp. 190-199.
    • (2007) Lecture Notes in Computer Science , vol.4472 , pp. 190-199
    • Bicego, M.1    Pȩkalska, E.2    Duin, R.3
  • 44
    • 67649395203 scopus 로고    scopus 로고
    • Augmented statistical models: Exploiting generative models in discriminative classifiers
    • M. Layton, M. Gales, Augmented statistical models: exploiting generative models in discriminative classifiers, in: Advances in Neural Information Processing Systems, 2005.
    • (2005) Advances in Neural Information Processing Systems
    • Layton, M.1    Gales, M.2
  • 51
    • 0002935122 scopus 로고    scopus 로고
    • Combining support vector and mathematical programming methods for induction
    • Schölkopf B., Burges C., and Smola A. (Eds), MIT Press, Cambridge, MA
    • Bennett K. Combining support vector and mathematical programming methods for induction. In: Schölkopf B., Burges C., and Smola A. (Eds). Advances in Kernel Methods-SV Learning (1999), MIT Press, Cambridge, MA 307-326
    • (1999) Advances in Kernel Methods-SV Learning , pp. 307-326
    • Bennett, K.1
  • 54
    • 12244293651 scopus 로고    scopus 로고
    • Simultaneous classification and relevant feature identification in high-dimensional spaces: application to molecular profiling data
    • Bhattacharyya C., Grate L., Rizki A., Radisky D., Molina F., Jordan M., Bissel M., and Mian I. Simultaneous classification and relevant feature identification in high-dimensional spaces: application to molecular profiling data. Signal Processing 83 (2003) 729-743
    • (2003) Signal Processing , vol.83 , pp. 729-743
    • Bhattacharyya, C.1    Grate, L.2    Rizki, A.3    Radisky, D.4    Molina, F.5    Jordan, M.6    Bissel, M.7    Mian, I.8
  • 55
    • 14644420142 scopus 로고    scopus 로고
    • Face recognition based on multi-class mapping of Fisher Scores
    • Chen L., Man H., and Nefian A.V. Face recognition based on multi-class mapping of Fisher Scores. Pattern Recognition 38 (2005) 799-811
    • (2005) Pattern Recognition , vol.38 , pp. 799-811
    • Chen, L.1    Man, H.2    Nefian, A.V.3
  • 56
    • 1642338802 scopus 로고    scopus 로고
    • Marginalised kernels for biological sequences
    • Tsuda K., Kin T., and Asai K. Marginalised kernels for biological sequences. Bioinformatics 18 (2002) 268-275
    • (2002) Bioinformatics , vol.18 , pp. 268-275
    • Tsuda, K.1    Kin, T.2    Asai, K.3
  • 57
    • 34547260222 scopus 로고    scopus 로고
    • Acoustic modelling using continuous rational kernels
    • Layton M., and Gales M. Acoustic modelling using continuous rational kernels. Journal of VLSI Signal Processing 48 (2007) 67-82
    • (2007) Journal of VLSI Signal Processing , vol.48 , pp. 67-82
    • Layton, M.1    Gales, M.2
  • 58
    • 0000396062 scopus 로고    scopus 로고
    • Natural gradient works efficiently in learning
    • Amari S. Natural gradient works efficiently in learning. Neural Computation 10 (1998) 251-276
    • (1998) Neural Computation , vol.10 , pp. 251-276
    • Amari, S.1
  • 60
    • 0030718403 scopus 로고    scopus 로고
    • Selecting the toroidal self-organizing feature maps (TSOFM) best organized to object recognition
    • G. Andreu, A. Crespo, J. Valiente, Selecting the toroidal self-organizing feature maps (TSOFM) best organized to object recognition, in: International Conference on Neural Networks, vol. 2, 1997, pp. 1341-1346.
    • (1997) International Conference on Neural Networks , vol.2 , pp. 1341-1346
    • Andreu, G.1    Crespo, A.2    Valiente, J.3
  • 63
    • 67649402758 scopus 로고    scopus 로고
    • M. Kadous, Learning comprehensible descriptions of multivariate time series, in: International Conference on Machine Learning, 1999, pp. 454-463.
    • M. Kadous, Learning comprehensible descriptions of multivariate time series, in: International Conference on Machine Learning, 1999, pp. 454-463.
  • 64
    • 0033220730 scopus 로고    scopus 로고
    • Multidimensional curve classification using passing-through regions
    • Kudo M., Toyama J., and Shimbo M. Multidimensional curve classification using passing-through regions. Pattern Recognition Letters 20 11-13 (1999) 1103-1111
    • (1999) Pattern Recognition Letters , vol.20 , Issue.11-13 , pp. 1103-1111
    • Kudo, M.1    Toyama, J.2    Shimbo, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.