Scaling large margin classifiers for spoken language understanding

Speech Communication

Volumn 48, Issue 3-4, 2006, Pages 239-261

  Haffner, Patrick ^a  

^a AT AND T LABS RESEARCH   (United States)

Author keywords

AdaBoost; Machine learning; Maximum entropy; Spoken language understanding; SVM

Indexed keywords

DATA MINING; DATA STORAGE EQUIPMENT; LEARNING ALGORITHMS; LEARNING SYSTEMS; ROBUSTNESS (CONTROL SYSTEMS); SEMANTICS;

ADABOOST; MAXIMUM ENTROPY; SPOKEN LANGUAGE UNDERSTANDING;

FORMAL LANGUAGES;

EID: 32144432078     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.specom.2005.06.008     Document Type: Article

References (52)

1. Abney, S., Schapire, R.E., Singer, Y., 1999. Boosting applied to tagging and PP attachment. In: Proc. Joint SIGDAT Conf. Empirical Methods in Natural Language Processing and Very Large Corpora.
2. Aiolli, F., Sperduti, A., 2005. Learning Preferences for Multiclass Problems. In: Advances in Neural Information Processing Systems, Vol. 17.
3. Bartlett, P.L., Collins, M., Taskar, B., McAllester, D., 2005. Exponentiated gradient algorithms for large-margin structured classification. In: Advances in Neural Information Processing Systems, Vol. 17. pp. 113-120.
4. Begeja, L., Drucker, H., Gibbon, D., Haffner, P., Liu, Z., Renger, B., Shahraray, B., March 2005. Semantic data mining of short utterances. In: Proc. ICASSP'05, Philadelphia, PA.
5. Bordes, A., Ertekin, S., Weston, J., Bottou, L., May 2005. Working document: Fast kernel classifiers with online and active learning. Available from: 〈http://leon.bottou.com/publications〉.
6. L. Breiman Prediction games and arcing algorithms Neural Comput. 11 7 1999 1493 1517
7. Chang, C.-C., Lin, C.-J., 2001. LIBSVM: a library for support vector machines. Available from: 〈http://www.csie.ntu.edu.tw/cjlin/libsvm〉.
8. Chelba, C., Acero, A., 2003. Discriminative training of N-gram classifiers for speech and text routing. In: Proc. INTERSPEECH'03 - Eurospeech, Geneva, Switzerland.
9. Collins, M., Schapire, R.E., Singer, Y., 2000. Logistic regression, AdaBoost and Bregman distances. In: Proc. COLT'00, Stanford, CA, pp. 158-169.
10. R. Collobert, and S. Bengio SVMTorch: Support vector machines for large-scale regression problems J. Machine Learn. Res. 1 2001 143 160
11. R. Collobert, S. Bengio, and Y. Bengio A parallel mixture of SVMs for very large scale problems Neural Comput. 14 5 2002
12. C. Cortes, P. Haffner, and M. Mohri Rational kernels: theory and algorithms J. Machine Learn. Res. 5 2004 1035 1062
13. K. Crammer, and Y. Singer On the algorithmic implementation of multiclass kernel-based vector machines J. Machine Learn. Res. 2 2002 265 292
14. N. Cristianini, and J. Shawe-Taylor An Introduction to Support Vector Machines 2000 Cambridge University Press Cambridge UK
15. DiFabbrizio, G., Tur, G., Hakkani-Tur, D., May 2004. Bootstrapping spoken dialog systems with data reuse. In: Proceedings of the 5th SIGdial Workshop on Discourse and Dialogue.
16. M. Dudik, S. Phillips, and R.E. Schapire Performance guarantees for regularized maximum entropy density estimation Proc. COLT'04 2004 Springer-Verlag Banff, Canada
17. Freund, Y., Schapire, R.E., 1996. Experiments with a new boosting algorithm. In: Proc. ICML'96, pp. 148-156.
18. Friedman, J., Hastie, T., Tibshirani, R., 1998. Additive logistic regression: a statistical view of boosting.
19. A.L. Gorin, G. Riccardi, and J.H. Wright How may I help you? Speech Comm. 23 1997 113 127
20. Graf, H.P., Cosatto, E., Bottou, L., Durdanovic, I., 2005. Parallel support vector machines: the cascade SVM. In: Advances in Neural Information Processing Systems, Vol. 17.
21. Gupta, N., Bangalore, S., 2002. Extracting clauses for spoken language understanding in conversational systems. In: Proc. EMNLP'02.
22. Haffner, P., Tur, G., Wright, J., 2003. Optimizing SVMs for complex Call Classification. In: Proc. ICASSP'03, Hong Kong.
23. Haffner, P., Phillips, S., Schapire, R., 2005a. Efficient multiclass implementations of regularized maximum entropy. Available from: 〈http://www.research.att.com/~haffner〉.
24. Haffner, P., Sen, S., Spatscheck, O., Wang, D., 2005b. ACAS: automated construction of application signatures. In: Workshop on Mining Network Data(MineNet-05).
25. Joachims, T., 1998a. Making large-scale SVM learning practical. In: Advances in Kernel Methods-Support Vector Learning.
26. T. Joachims Text categorization with support vector machines: learning with many relevant features Proc. ECML-98 1998 Springer-Verlag
27. S.S. Keerthi, K. Duan, S.K. Shevade, and A.N. Poo A fast dual algorithm for kernel logistic regression Proc. ICML'02 2002 Morgan Kaufmann Sydney, Australia 299 306
28. Lebanon, G., Lafferty, J., 2002. Boosting and maximum likelihood for exponential models. In: Advances in Neural Information Processing Systems, Vol. 14.
29. Levit, M., Haffner, P., Gorin, A., Alshawi, H., Noth, E., 2004. Aspects of named entity processing. In: Proc. INTERSPEECH'04.
30. O.L. Mangasarian, and D.R. Musicant Lagrangian support vector machines J. Machine Learn. Res. 1 2001 161 177
31. Ng, A.Y., Jordan, M.I., 2002. On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. In: Advances in Neural Information Processing Systems, Vol. 14, pp. 841-848.
32. Nigam, K., Lafferty, J., McCallum, A., 1999. Using maximum entropy for text classification. In: IJCAI-99 Workshop on Machine Learning for Information Filtering, pp. 61-67.
33. Phillips, S., Haffner, P., 2005. Accelerating sequential learning with partial pricing, in preparation.
34. S. Phillips, M. Dudik, and R.E. Schapire A maximum entropy approach to species distribution modelling Proc. ICML'04 2004 Omni Press Banff, Canada
35. J. Platt Fast training of support vector machines using sequential minimal optimization B. Schlkopf C. Burges A. Smola Advances in Kernel Methods-Support Vector Learning 1998 MIT Press
36. Ratsch, G., Warmuth, M., 2002. Maximizing the margin with boosting. In: Proc. COLT'02, Sydney, Australia, pp. 334-350.
37. R. Rifkin, and A. Klautau In defense of one-vs-all classification J. Machine Learn. Res. 1 2004 101 141
38. R.E. Schapire, and Y. Singer Improved boosting algorithms using confidence-rated predictions Machine Learn. 37 3 1999 297 336
39. R.E. Schapire, and Y. Singer Boostexter: a boosting-based system for text categorization Machine Learn. 39 2/3 2000 135 168
40. R.E. Schapire, Y. Freund, P. Bartlett, and W.S. Lee Boosting the margin: a new explanation for the effectiveness of voting methods Proc. ICML'97 1997 Morgan Kaufmann 322 330
41. B. Schölkopf, and A. Smola Learning with Kernels 2002 MIT Press Cambridge, MA
42. Steinwart, I., 2004. Sparseness of support vector machines - some asymptotically sharp bounds. In: Advances in Neural Information Processing Systems, Vol. 16.
43. Tang, M., Pellom, B., Hacioglu, K., 2003. Call-type classification and unsupervised training for the call center domain. In: Proc. ASRU-03.
44. Tipping, M., 2000. The relevance vector machine. In: Advances in Neural Information Processing Systems, Vol. 12.
45. I.W. Tsang, J.T. Kwok, and P.-M. Cheung Core vector machines: fast SVM training on very large data sets J. Machine Learn. Res. 6 2005 363 392
46. Tur, G., Wright, J., Gorin, A., Riccardi, G., Hakkani-Tnr, D., 2002. Improving spoken language understanding using word confusion networks. In: Proc. ICSLP'02.
47. Tur, G., Rahim, M., Hakkani-Tur, D., 2003. Active labeling for spoken language understanding. In: Proc. INTERSPEECH'03-Eurospeech, Geneva, Switzerland.
48. Tur, G., Hakkani-Tur, D., Riccardi, G., 2004. Extending boosting for call classification using word confusion networks. In: Proc. ICASSP'04.
49. V.N. Vapnik Statistical Learning Theory 1998 John Wiley & Sons
50. Vural, V., Dy, J., 2004. A hierarchical method for multiclass support vector machines. In: Proc. ICML'04, Omnipress, Banff, Canada.
51. Weston, J., Watkins, C., 1998. Multi-class support vector machines. Report CSD-TR-98-04, Department of Computer Science, Royal Holloway, University of London, Egham, TW20 0EX, UK, 1998.
52. J. Zhang, R. Jin, Y. Yang, and A.G. Hauptmann Modified logistic regression: an approximation to SVM and its applications in large-scale text categorization Proc. ICML'03 2003 Morgan Kaufmann Washington DC