Learning ensembles of structured prediction rules

52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference

Volumn 1, Issue , 2014, Pages 1-12

  Cortes, Corinna ^a   Kuznetsov, Vitaly ^b   Mohri, Mehryar ^a  

^a GOOGLE INC   (United States)

^b NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; FORECASTING;

DETERMINISTIC ALGORITHMS; PRIOR KNOWLEDGE; STRUCTURED PREDICTION; THEORETICAL GUARANTEES;

LEARNING ALGORITHMS;

EID: 84906932481     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/v1/p14-1001     Document Type: Conference Paper

References (40)

1. Leo Breiman. 1996. Bagging predictors. Machine Learning, 24(2):123-140.
2. R. Caruana, A. Niculescu-Mizil, G. Crew, and A. Ksikes. 2004. Ensemble selection from libraries of models. In Proceedings of ICML, pages 18-.
3. N. Cesa-Bianchi, A. Conconi, and C. Gentile. 2004. On the generalization ability of on-line learning algorithms. IEEE Transactions on Information Theory, 50(9):2050-2057.
4. Michael Collins and Terry Koo. 2005. Discriminative reranking for natural language parsing. Computational Linguistics, 31(1):25-70.
5. M. Collins. 2002. Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. In Proceedings of ACL, pages 1-8.
6. C. Cortes, M. Mohri, and J. Weston. 2005. A general regression technique for learning transductions. In Proceedings of ICML 2005, pages 153-160, New York, NY, USA. ACM.
7. Corinna Cortes, Vitaly Kuznetsov, and Mehryar Mohri. 2014a. Ensemble methods for structured prediction. In Proceedings of ICML.
8. Corinna Cortes, Mehryar Mohri, and Umar Syed. 2014b. Deep boosting. In Proceedings of the Fourteenth International Conference on Machine Learning (ICML 2014).
9. O. Dekel and Y. Singer. 2005. Data-driven online to batch conversion. In Advances in NIPS 18, pages 1207-1216.
10. Jonathan G Fiscus. 1997. Postprocessing system to yield reduced word error rates: Recognizer output voting error reduction (rover). In Proceedings of the 1997 IEEE ASRU Workshop, pages 347-354, Santa Barbara, CA.
11. Y. Freund and R. Schapire. 1997. A decision-theoretic generalization of on-line learning and application to boosting. Journal of Computer and System Sciences, 55(1):119-139.
12. Yoav Freund, Yishay Mansour, and Robert E. Schapire. 2004. Generalization bounds for averaged classifiers. The Annals of Statistics, 32:1698-1722.
13. Arnab Ghoshal, Martin Jansche, Sanjeev Khudanpur, Michael Riley, and Morgan Ulinski. 2009. Web-derived pronunciations. In Proceedings of ICASSP, pages 4289-4292.
14. T. Joachims. 2008. Support vector machines for complex outputs.
15. A. Kalai and S. Vempala. 2005. Efficient algorithms for online decision problems. Journal of Computer and System Sciences, 71(3):291-307.
16. D. Kocev, C. Vens, J. Struyf, and S. Deroski. 2013. Tree ensembles for predicting structured outputs. Pattern Recognition, 46(3):817-833, March.
17. Vladmir Koltchinskii and Dmitry Panchenko. 2002. Empirical margin distributions and bounding the generalization error of combined classifiers. Annals of Statistics, 30.
18. J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of ICML, pages 282-289.
19. N. Littlestone and M. Warmuth. 1994. The weighted majority algorithm. Information and Computation, 108(2):212-261.
20. N. Littlestone. 1989. From on-line to batch learning. In Proceedings of COLT 2, pages 269-284.
21. David J. C. MacKay. 1991. Bayesian methods for adaptive models. Ph.D. thesis, California Institute of Technology.
22. David J.C. MacKay. 1997. Ensemble learning for hidden markov models. Technical report, Cavendish Laboratory, Cambridge UK.
23. Mehryar Mohri, Fernando C. N. Pereira, and Michael Riley. 2008. Speech recognition with weighted finite-state transducers. In Handbook on Speech Processing and Speech Communication, Part E: Speech recognition. Springer-Verlag.
24. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 2012. Foundations of Machine Learning. The MIT Press.
25. Mehryar Mohri. 1997. Finite-state transducers in language and speech processing. Computational Linguistics, 23(2):269-311.
26. N. Nguyen and Y. Guo. 2007. Comparison of sequence labeling algorithms and extensions. In Proceedings of ICML, pages 681-688.
27. N. Okazaki. 2007. CRFsuite: a fast implementation of conditional random fields (crfs).
28. Slav Petrov. 2010. Products of random latent variable grammars. In HLT-NAACL, pages 19-27.
29. A. Rafferty, A. Kleeman, J. Finkel, and C. Manning. 2014. Stanford classifer.
30. K. Sagae and A. Lavie. 2006. Parser combination by reparsing. In Proceedings of HLT/NAACL, pages 129-132.
31. Robert E. Schapire and Yoram Singer. 1999. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297-336.
32. Robert E. Schapire and Yoram Singer. 2000. Boostexter: A boosting-based system for text categorization. Machine Learning, 39(2-3):135-168.
33. Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee. 1997. Boosting the margin: A new explanation for the effectiveness of voting methods. In ICML, pages 322-330.
34. Padhraic Smyth and David Wolpert. 1999. Linearly combining density estimators via stacking. Machine Learning, 36:59-83, July.
35. E. Takimoto and M. K. Warmuth. 2003. Path kernels and multiplicative updates. JMLR, 4:773-818.
36. B. Taskar, C. Guestrin, and D. Koller. 2004. Max-margin Markov networks. In Advances in NIPS 16. MIT Press, Cambridge, MA.
37. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. 2005. Large margin methods for structured and interdependent output variables. JMLR, 6:1453-1484, December.
38. Q. Wang, D. Lin, and D. Schuurmans. 2007. Simple training of dependency parsers via structured boosting. In Proceedings of IJCAI 20, pages 1756-1762.
39. D. Zeman and Z. Žabokrtský. 2005. Improving parsing accuracy by combining diverse dependency parsers. In Proceedings of IWPT 9, pages 171-178.
40. H. Zhang, M. Zhang, C. Tan, and H. Li. 2009. K-best combination of syntactic parsers. In Proceedings of EMNLP: Volume 3, pages 1552-1560.