Discriminative unsupervised learning of structured predictors

ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning

Volumn 2006, Issue , 2006, Pages 1057-1064

  Xu, Linli ^a   Wilkinson, Dana ^a   Southey, Finnegan ^b   Schuurmans, Dale ^b  

^a UNIVERSITY OF WATERLOO   (Canada)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER PROGRAMMING; COSTS; MARKOV PROCESSES; MATHEMATICAL MODELS; PERSONNEL TRAINING;

BAUM-WELCH TRAINING; HIDDEN MARKOV MODELS; MARKOV NETWORKS; STRUCTURED PREDICTORS;

LEARNING SYSTEMS;

EID: 33749242077     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Altun, Y., McAllester, D., & Belkin, M. (2005). Maximum margin semi-supervised learning for structured variables. Proceedings NIPS 18.
2. Altun, Y., Tsochantaridis, I., & Hofmann, T. (2003). Hidden Markov support vector machines. Proc. ICML.
3. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge U. Press.
4. Crammer, K., & Singer, Y. (2001). On the algorithmic interpretation of multiclass kernel-based vector machines. JMLR, 2.
5. De Bie, T., & Cristianini, N. (2003). Convex methods for transduction. Proceedings NIP 16.
6. Durbin, B., Eddy, S., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis. Cambridge U. Press.
7. Helmberg, C. (2000). Semidefinite programming for combinatorial optimization (Technical Report).
8. Jelinek, F. (1998). Statistical methods for speech recognition. MIT Press.
9. Jurafsky, D., & Martin, J. (2000). Speech and language processing. Prentice Hall.
10. Lafferty, J., McCallum, A., & Pereira, F. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings ICML.
11. Laurent, M., & Poljak, S. (1995). On a positive semidefinite relaxation of the cut polytope. Linear Algebra and its Applications, 223/224.
12. Manning, C., & Schutze, H. (1999). Foundations of statistical natural language processing. MIT Press.
13. Nesterov, Y., & Nimirovskii, A. (1994). Interior-point polynomial algorithms in convex programming. SIAM.
14. Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proc. of IEEE, 77, 257-286.
15. Taskar, B., Guestrin, C., & Koller, D. (2003). Max-margin Markov networks. Proceedings NIPS 16.
16. Tsochantaridis, I., Hofmann, T., Joachims, T., & Altun, Y. (2004). Support vector machine learning for interdependent and structured output spaces. Proceedings ICML.
17. Xu, L., Neufeld, J., Larson, B., & Schuurmans, D. (2004). Maximum margin clustering. NIPS 17.
18. Xu, L., & Schuurmans, D. (2005). Unsupervised and semi-supervised multi-class support vector machines. Proceedings AAAI.