1. Bouchard G, Triggs B. The trade-off between generative and discriminative classifiers. Proceedings of International Symposium on Computational Statistics (COMPSTAT’04), Aug 22-27, 2004, Prague, Czech. 2004: 721-728
2. Andrew Y N, Michale I J. On discriminative vs generative classifiers: a comparison of logistic regression and naïve Bayes. Proceedings of Neural Information Processing System (NIPS’01), Dec 3-8, 2001, Vancouver, Canada. 2001: 841-848
3. Greiner R, Zhou W. Structural extension to logistic regression: discriminative parameter learning of belief net classifiers. Proceedings of Annual Meeting of the American Association for Artificial Intelligence (IAAI’02), Jul 28-Aug 1, 2002, Edmonton, Canada. 2002: 167-173
4. Grossman D, Domingos P. Learning Bayesian network classifiers by maximizing conditional likelihood. Proceedings of the 21st International Conference on Machine Learning (ICML’04), Jul 4-8, 2004, Banff, Canada. New York, NY, USA: ACM, 2004: 361-368
5. Kapadia, S. Discriminative training of hidden Markov models. Cambridge, UK: Cambridge University, 1998
6. Woodland P C, Povey D. Large scale discriminative training of hidden Markov models for speech recognition. Compute Speech and Language, 2002, 1(16): 25-47
7. Valtchev V, Odell J J, Woodland P C, et al. MMIE training of large vocabulary recognition systems. Speech Communication, 1997, 22(4): 303-314
8. Juang B H, Hou W, Lee C H. Minimum classification error rate methods for speech recognition. IEEE Transactions on Speech and Audio Processing, 1997, 5(3): 257-265
9. Povey D, Woodland P C. Minimum phone error and I-smoothing for improved discriminative training. Proceedings of the 27th International Conference on Acoustics, Speech and Signal Processing (ICASSP’02): Vol 1, May 13-17, 2002, Orlando, FL, USA. Piscataway, NJ, USA: IEEE, 2002: 105-108
10. Povey D. Discriminative training for large vocabulary speech recognition. Cambridge, UK: Cambridge University, 2004
11. Jiang H, Li X W, Liu C J. Large margin hidden Markov models for speech recognition. IEEE Transactions on Audio, Speech and Language Processing, 2006, 14(5): 1584-1595 |