Large margin methods for structured and interdependent output variables

Journal of Machine Learning Research

Volumn 6, Issue , 2005, Pages

  Tsochantaridis, Ioannis ^a   Joachims, Thorsten ^b   Hofmann, Thomas ^c   Altun, Yasemin ^d  

^a GOOGLE INC   (United States)

^b Department of Computer Science and School of Operations Research and Information Engineering   (United States)

^c DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

^d TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); INFORMATION RETRIEVAL; NATURAL LANGUAGE PROCESSING SYSTEMS; OPTICAL CHARACTER RECOGNITION; OPTIMIZATION; POLYNOMIALS; PROBLEM SOLVING; SET THEORY; TREES (MATHEMATICS);

CLASSIFICATION ALGORITHMS; COMPUTATIONAL BIOLOGY; INTERDEPENDENT OUTPUT VARIABLES; LARGE MARGIN METHODS;

LEARNING SYSTEMS;

EID: 24944537843     PISSN: 15337928     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (34)

1. Y. Altun, I. Tsochantaridis, and T. Hofmann. Hidden Markov support vector machines. In Proceedings of the Twentieth International Conference on Machine Learning, 2003.
2. L. Cai and T. Hofmann. Hierarchical document categorization with support vector machines. In Proceedings of the ACM Thirteenth Conference on Information and Knowledge Management, 2004.
3. W. Cohen, R. Shapire, and Y. Singer. Learning to order things. Journal of Artificial Intelligence Research, 10:243-270, 1999.
4. M. Collins. Discriminative reranking for natural language parsing. In Proceedings of the Seventieth International Conference on Machine Learning, 2000.
5. M. Collins. Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2002.
6. M. Collins and N. Duffy. Convolution kernels for natural language. In Advances in Neural Information Processing Systems 14, pages 625-632, 2002a.
7. M. Collins and N. Duffy. New ranking algorithms for parsing and tagging: kernels over discrete structures, and the voted perceptron. In Proceedings of the Fortieth Annual Meeting of the Association for Computational Linguistics, 2002b.
8. K. Crammer and Y. Singer. On the algorithmic implementation of multi-class kernel-based vector machines. Machine Learning Research, 2:265-292, 2001.
9. K. Crammer and Y. Singer. Pranking with ranking. In Advances in Neural Information Processing Systems 14, 2002.
10. R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis. Cambridge University Press, 1998.
11. G. D. Forney Jr. The Viterbi algorithm. Proceedings of the IEEE, 61:268-278, 1973.
12. M. Grötschel, L. Lovàtz, and A. Schrijver. The ellipsoid method and its consequences in combinatorial optimization. Combinatorica, 1(2): 169-197, 1981.
13. S. Har-Peled, D. Roth, and D. Zimak. Constraint classification for multiclass classification and ranking. In Advances in Neural Information Processing Systems 14, 2002.
14. R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. In Advances in Large Margin Classifiers. MIT Press, Cambridge, MA, 2000.
15. T. Hofmann, I. Tsochantaridis, and Y. Altun. Learning over structured output spaces via joint kernel functions. In Proceedings of the Sixth Kernel Workshop, 2002.
16. T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining, 2002.
17. T. Joachims. Learning to align sequences: A maximum-margin approach. Technical report, Cornell University, 2003.
18. T. Joachims. A support vector method for multivariate performance measures. In Proceedings of the Twenty-Second International Conference on Machine Learning, 2005.
19. M. Johnson. PCFG models of linguistic tree representations. Computational Linguistics, 24(4):613-632, 1998.
20. N. Karmarkar. A new polynomial-time algorithm for linear programming. Combinatorica, 4(4):373-396, 1984.
21. J. E. Kelley. The cutting-plane method for solving convex programs. Journal of the Society for Industrial Applied Mathematics, 8:703-712, 1960.
22. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the Eighteenth International Conference on Machine Learning, pages 282-289, 2001.
23. C. D. Manning and H. Schuetze. Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, MA, 1999.
24. E. Ristad and P. Yianilos. Learning string edit distance. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 287-295, 1997.
25. R. E. Schapire and Y. Singer. Boostexter: A boosting-based system for text categorization. Machine Learning, 39(2-3): 135-168, 2000.
26. R. Schwarz and Y. L. Chow. The n-best algorithm: An efficient and exact procedure for finding the n most likely hypotheses. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pages 81-84, 1990.
27. A. Smola and T. Hofmann. Exponential families for generative and discriminative estimation. Technical report, 2003.
28. B. Taskar, C. Guestrin, and D. Koller. Max-margin Markov networks. In Advances in Neural Information Processing Systems 16, 2004a.
29. B. Taskar, D. Klein, M. Collins, D. Koller, and C. Manning. Max-Margin parsing. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2004b.
30. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In Proceedings of the Twenty-First International Conference on Machine Learning, 2004.
31. V. Vapnik. Statistical Learning Theory. Wiley and Sons Inc., New York, 1998.
32. J. Weston, O. Chapelle, A. Elisseeff, B. Schölkopf, and V. Vapnik. Kernel dependency estimation. In Advances in Neural Information Processing Systems 15, 2003.
33. J. Weston and C. Watkins. Multi-class support vector machines. Technical Report CSD-TR-98-04, Department of Computer Science, Royal Holloway, University of London, 1998.
34. 3. Information and Control, 2(10): 189-208, 1967.