Structured prediction, dual extragradient and bregman projections

Journal of Machine Learning Research

Volumn 7, Issue , 2006, Pages 1627-1653

  Taskar, Ben ^a   Lacoste Julien, Simon ^a   Jordan, Michael I ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bregman projections; Extragradient; Large margin methods; Markov networks; Structured prediction

Indexed keywords

ALGORITHMS; COMBINATORIAL CIRCUITS; CONVERGENCE OF NUMERICAL METHODS; ESTIMATION; IMAGE SEGMENTATION; MARKOV PROCESSES; PROBLEM SOLVING; PROJECTION SYSTEMS; QUADRATIC PROGRAMMING;

BREGMAN PROJECTIONS; EXTRAGRADIENT; LARGE-MARGIN METHODS; MARKOV NETWORKS; STRUCTURED PREDICTION;

ELECTRIC NETWORK ANALYSIS;

EID: 33745771086     PISSN: 15337928     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (42)

1. Yasemin Altun, Ioannis Tsochantaridis, and Thomas Hofmann. Hidden Markov support vector machines. In International Conference on Machine Learning. ACM Press, New York, 2003.
2. Drago Anguelov, Ben Taskar, Vassil Chatalbashev, Daphne Koller, Dinkar Gupta, Geremey Heitz, and Andrew Y. Ng. Discriminative learning of Markov random fields for segmentation of 3D scan data. In International Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, Washington, DC, 2005.
3. Pierre Baldi, Jianlin Cheng, and Alessandro Vullo. Large-scale prediction of disulphide bond connectivity. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, 2005.
4. Peter L. Bartlett, Michael Collins, Ben Taskar, and David McAllester. Exponentiated gradient algorithms for large-margin structured classification. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, 2005.
5. Serge Belongie, Jitendra Malik, and Jan Puzicha. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machince Intelligence, 24:509-522, 2002.
6. Dimitri P. Bertsekas. Network Optimization: Continuous and Discrete Models. Athena Scientific, Belmont, MA, 1998.
7. Dimitri P. Bertsekas, Lazaros C. Polymenakos, and Paul Tseng. An ε-relaxation method for separable convex cost network flow problems. SIAM Journal on Optimization, 7(3):853-870, 1997.
8. Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
9. Chandra Chekuri, Sanjeev Khanna, Joseph Naor, and Leonid Zosin. Approximation algorithms for the metric labeling problem via a new linear programming formulation. In Proceedings of the 12th Annual ACM-SIAM Symposium on Discrete Algorithms, Washington, DC, 2001.
10. Chandra Chekuri, Sanjeev Khanna, Joseph Naor, and Leonid Zosin. A linear programming formulation and approximation algorithms for the metric labeling problem. SIAM Journal on Discrete Mathematics, 18(3):606-635, 2005.
11. Michael Collins. Discriminative training methods for hidden Markov models: Theory and experiments with perception algorithms. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Philadelphia, PA, 2002.
12. Richard Durbin, Sean Eddy, Anders Krogh, and Graeme Mitchison. Biological Sequence Analysis. Cambridge University Press, UK, 1998.
13. D. M. Greig, B. T. Porteous, and A. H. Seheult. Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society B, 51:271-279, 1989.
14. Francesca Guerriero and Paul Tseng. Implementation and test of auction methods for solving generalized network flow problems with separable convex cost. Journal of Optimization Theory and Applications, 115:113-144, 2002.
15. Trevor Hastie, Saharon Rosset, Robert Tibshirani, and J. Zhu. The entire regularization path for the support vector machine. Journal of Machine Learning Research, 5:1391-1415, 2004.
16. Bingsheng He and Li-Zhi Liao. Improvements of some projection methods for monotone nonlinear variational inequalities. Journal of Optimization Theory and Applications, 112:111-128, 2002.
17. Mark Jerrum and Alistair Sinclair. Polynomial-time approximation algorithms for the Ising model. SIAM Journal on Computing, 22:1087-1116, 1993.
18. Vladimir Kolmogorov and Martin Wainwright. On the optimality of tree-reweighted max-product message passing. In Uncertainty in Artificial Intelligence: Proceedings of the Twenty-First Conference. Morgan Kaufmann, San Mateo, CA, 2005.
19. Vladimir Kolmogorov and Ramin Zabih. What energy functions can be minimized using graph cuts? IEEE Transactions on Pattern Analysis and Machince Intelligence, 26:147-159, 2004.
20. G. M. Korpelevich. The extragradient method for finding saddle points and other problems. Ekonomika i Matematicheskie Metody, 12:747-756, 1976.
21. Sanjiv Kumar and Martial Hebert. Discriminative fields for modeling spatial dependencies in natural images. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, 2004.
22. Simon Lacoste-Julien, Ben Taskar, Dan Klein, and Michael I. Jordan. Word alignment via quadratic assignment. In Human Language Technology-North American Association for Computational Linguistics, New York, NY, 2006.
23. John Lafferty, Andrew McCallum, and Fernando Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In International Conference on Machine Learning. Morgan Kaufmann, San Mateo, CA, 2001.
24. John Lafferty, Xiaojin Zhu, and Yan Liu. Kernel conditional random fields: Representation and clique selection. In International Conference on Machine Learning. ACM Press, New York, 2004.
25. Yan LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86:2278-2324, 1998.
26. Christopher Manning and Hinrich Schütze. Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, MA, 1999.
27. Evgeny Matusov, Richard Zens, and Herman Ney. Symmetric word alignments for statistical machine translation. In Proceedings of the Twentieth International Conference on Computational Linguistics, Geneva, Switzerland, 2004.
28. Rada Mihalcea and Ted Pedersen. An evaluation exercise for word alignment. In Human Language Technology-North American Association for Computational Linguistics, Edmonton, Canada, 2003.
29. Yurii Nesterov. Dual extrapolation and its application for solving variational inequalites and related problems. Technical report, CORE, Catholic University of Louvain, 2003.
30. Franz Och and Herman Ney. A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1): 19-52, 2003.
31. John C. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schlkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods-Support Vector Learning, pages 185-208. MIT Press, Cambridge, MA, 1999.
32. Saharon Rosset. Tracking curved regularized optimization solution paths. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, 2004.
33. Alexander Schrijver. Combinatorial Optimization: Polyhedra and Efficiency. Springer, Berlin, 2003.
34. Ben Taskar. Learning Structured Prediction Models: A Large Margin Approach. PhD thesis, Stanford University, 2004.
35. Ben Taskar, Vassil Chatalbashev, and Daphne Koller. Learning associative Markov networks. In International Conference on Machine Learning. ACM Press, New York, 2004a.
36. Ben Taskar, Carlos Guestrin, and Daphne Koller. Max margin Markov networks. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, 2004b.
37. Ben Taskar, Vassil Chatalbashev, Daphne Koller, and Carlos Guestrin. Learning structured prediction models: a large margin approach. In International Conference on Machine Learning. ACM Press, New York, 2005a.
38. Ben Taskar, Simon Lacoste-Julien, and Dan Klein. A discriminative matching approach to word alignment. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Vancouver, CA, 2005b.
39. Ben Taskar, Simon Lacoste-Julien, and Michael I. Jordan. Structured prediction via the extragradient method. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, 2006.
40. 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. ACM Press, New York, 2004.
41. Leslie G. Valiant. The complexity of computing the permanent. Theoretical Computer Science, 8: 189-201, 1979.
42. Martin Wainwright, Tommi Jaakkola, and Alan Willsky. Map estimation via agreement on (hyper)trees: Message-passing and linear programming approaches. In Proceedings of the Allerton Conference on Communication, Control and Computing, Allerton, IL, 2002.