Maximum entropy discrimination markov networks

Journal of Machine Learning Research

Volumn 10, Issue , 2009, Pages 2531-2569

  Zhu, Jun ^a   Xing, Eric P ^a  

^a Carnegie Mellon University   (United States)

Author keywords

Entropie regularization; Graphical models; L1 regularization; Maximum entropy discrimination; Maximum margin markov network; Structured input output model

Indexed keywords

GRAPHICAL MODEL; INPUT/OUTPUT; MARKOV NETWORKS; MAXIMUM ENTROPY; MAXIMUM MARGIN; MAXIMUM MARGIN MARKOV NETWORK;

BAYESIAN NETWORKS; COMBINES; ENTROPY; GRAPHIC METHODS; HIDDEN MARKOV MODELS; INFERENCE ENGINES; LEARNING ALGORITHMS; SPEECH RECOGNITION; VARIATIONAL TECHNIQUES;

MAXIMUM LIKELIHOOD;

EID: 73549086344     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (50)

1. Yasemin Altun, Ioannis Tsochantaridis, and Thomas Hofmann. Hidden Markov support vector machines. In International Conference on Machine Learning, 2003.
2. Yasemin Altun, David McAllester, and Mikhail Belkin. Maximum margin semi-supervised learning for structured variables. In Advances in Neural Information Processing Systems, 2006.
3. 1 -regularized log-linear models. In International Conference on Machine Learning, 2007.
4. David F. Andrews and Colin L. Mallows. Scale mixtures of normal distributions. Journal of the Royal Statistical Society, B(1):99-102, 1974.
5. Peter L. Bartlett, Michael Collins, Ben Taskar, and David McAllester. Exponentiated gradient algorithms for larg- margin structured classification. In Advances in Neural Information Processing Systems, 2004.
6. Kristin P. Bennett and O. L. Mangasarian. Robust linear programming discrimination of two linearly inseparable sets. Optim. Methods Softw, (1):23-34, 1992.
7. Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
8. Ulf Brefeld and Tobias Scheffer. Semi-supervised learning for structured output variables. In International Conference on Machine Learning, 2006.
9. Antoni B. Chan, Nuno Vasconcelos, and Gert R. G. Lanckriet. Direct convex relaxations of sparse SVM. In International Conference on Machine Learning, 2007.
10. Mark Dredze, Koby Crammer, and Fernando Pereira. Confidence-weighted linear classification. In International Conference on Machine Learning, 2008.
11. 1-ball for learning in high dimensions. In International Conference on Machine Learning, 2008.
12. Mirosla Dudfk, Steven J. Phillips, and Robert E. Schapire. Maximum entropy density estimation with generalized regularization and an application to species distribution modeling. Journal of Machine Learning Research, (8):1217-1260, 2007.
13. Susana Eyheramendy, Alexander Genkin, Wen-Hua Ju, David D. Lewis, and David Madiagan. Sparse Bayesian classifiers for text categorization. Technical report, Rutgers University, 2003.
14. Mario Figueiredo. Adaptive sparseness for supervised learning. IEEE Trans. on Pattern Analysis and Machine Intelligence, 25(9): 1150-1159, 2003.
15. Mario A. T. Figueiredo. Adaptive sparseness using Jeffreys prior. In Advances in Neural Information Processing Systems, 2001.
16. Amir Globerson, Terry Y. Koo, Xavier Carreras, and Michael Collins. Exponentiated gradient algorithms for log-linear structured prediction. In International Conference on Machine Learning, 2007.
17. Tommi Jaakkola, Marina Meila, and Tony Jebara. Maximum entropy discrimination. In Advances in Neural Information Processing Systems, 1999.
18. Tony Jebara. Multitask sparsity via maximum entropy discrimination. To appear in Journal of Machine Learning Research, 2009.
19. Tony Jebara and Tommi Jaakkola. Feature selection and dualities in maximum entropy discrimination. In Uncertainty in Artificial Intelligence, 2000.
20. Ata Kaban. On Bayesian classification with laplace priors. Pattern Recognition Letters, 28(10): 1271-1282, 2007.
21. John Lafferty, Andrew McCallum, and Fernando Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In International Conference on Machine Learning, 2001.
22. John Langford and John Shawe-Taylor. Pac-Bayes & margins. In Advances in Neural Information Processing Systems, 2003.
23. John Langford, Matthias Seeger, and Nimrod Megiddo. An improved predictive accuracy bound for averaging classifiers. In International Conference on Machine Learning, 2001.
24. Guy Lebanon and John Lafferty. Boosting and maximum likelihood for exponential models. In Advances in Neural Information Processing Systems, 2001.
25. 1 -regularization. In Advances in Neural Information Processing Systems, 2006.
26. Dong C. Liu and Jorge Nocedal. On the limited memory BFGS method for large scale optimization. Mathematical Programming, (45):503-528, 1989.
27. David McAllester. Generalization bounds and consistency for structured labeling. In Predicting Structured Data, edited by G. Bakir, T. Hofmann, B. Scholkopf, A. Smola, B. Taskar, and S. V. N. Vishwanathan. MIT Press, 2007.
28. David McAllester. PAC-Bayesian model averaging. In the Twelfth Annual Conference on Computational Learning Theory, 1999.
29. Yuan (Alan) Qi, Martin Szummer, and Thomas P. Minka. Bayesian conditional random fields. In International Conference on Artificial Intelligence and Statistics, 2005.
30. Ariadna Quattoni, Michael Collins, and Trevor Darrell. Conditional random fields for object recognition. In Advances in Neural Information Processing Systems, 2004.
31. Lawrence R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, (77(2)):257-286,1989.
32. Nathan D. Ratliff, J. Andrew Bagnell, and Martin A. Zinkevich. (online) subgradient methods for structured prediction. In International Conference on Artificial Intelligence and Statistics, 2007.
33. Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5):1651-1686, 1998.
34. Peter Sollich. Bayesian methods for support vector machines: Evidence and predictive class probabilities. Journal of Machine Learning Research, (3):21-52, 2002.
35. Ben Taskar, Carlos Guestrin, and Daphne Koller. Max- margin Markov networks. In Advances in Neural Information Processing Systems, 2003.
36. Ben Taskar, Simon Lacoste-Julien, and Michael I. Jordan. Structured prediction via the extragradient method. In Advances in Neural Information Processing Systems, 2006.
37. Robert Tibshirani, Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society, B (58):267-288, 1996.
38. Michael E. Tipping. Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research, (1):211-244, 2001.
39. Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, and Yasemin Altun. Support vector machine learning for interdependent and structured output spaces. In International Conference on Machine Learning, 2004.
40. 1 -regularized logistic regression. In Advances in Neural Information Processing Systems, 2006.
41. David Wipf, Jason Palmer, and Bhaskar Rao. Perspectives on sparse bayesian learning. In Advances in Neural Information Processing Systems, 2003.
42. Linli Xu, Dana Wilkinson, Finnegan Southey, and Dale Schuurmans. Discriminative unsupervised learning of structured predictors. In International Conference on Machine Learning, 2006.
43. Zhihua Zhang and Michael I. Jordan. Bayesian multicategory support vector machines. In Uncertainty in Artificial Intelligence, 2006.
44. Ji Zhu, Sanaron Rosset, Trevor Hastie, and Rob Tibshirani. 1-norm support vector machines. In Advances in Neural Information Processing Systems, 2004.
45. Jun Zhu and Eric P. Xing. On primal and dual sparsity of Markov networks. In International Conference on Machine Learning, 2009.
46. Jun Zhu, Zaiqing Nie, Bo Zhang, and Ji-Rong Wen. Dynamic hierarchical Markov random fields for integrated web data extraction. Journal of Machine Learning Research, (9): 1583-1614, 2008a.
47. Jun Zhu, Eric P. Xing, and Bo Zhang. Laplace maximum margin Markov networks. In International Conference on Machine Learning, 2008b.
48. Jun Zhu, Eric P. Xing, and Bo Zhang. Partially observed maximum entropy discrimination Markov networks. In Advances in Neural Information Processing Systems, 2008c.
49. Jun Zhu, Amr Ahmed, and Eric P. Xing. MedLDA: Maximum margin supervised topic models for regression and classification. In International Conference on Machine Learning, 2009a.
50. Jun Zhu, Eric P. Xing, and Bo Zhang. Primal sparse max- margin Markov networks. In International Conference on Knowledge Discovery and Data Mining (KDD), 2009b.