Maximum margin bayesian network classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 34, Issue 3, 2012, Pages 521-532

  Pernkopf, Franz ^a   Wohlmayr, Michael ^a   Tschiatschek, Sebastian ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Bayesian network classifier; convex relaxation; discriminative classifiers; discriminative learning; large margin training; missing features

Indexed keywords

BAYESIAN NETWORK CLASSIFIERS; CONVEX RELAXATION; DISCRIMINATIVE CLASSIFIERS; DISCRIMINATIVE LEARNING; MISSING FEATURES;

CLASSIFIERS; CONJUGATE GRADIENT METHOD; LEARNING ALGORITHMS; MAXIMUM LIKELIHOOD ESTIMATION; OPTIMIZATION; PARAMETER ESTIMATION; RELAXATION PROCESSES; SUPPORT VECTOR MACHINES; TREES (MATHEMATICS);

BAYESIAN NETWORKS;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; HUMAN; LEARNING; METHODOLOGY; SPEECH;

ALGORITHMS; BAYES THEOREM; HUMANS; LEARNING; PATTERN RECOGNITION, AUTOMATED; SPEECH;

EID: 84856204760     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2011.149     Document Type: Article

References (43)

1. Y. Guo, D. Wilkinson, and D. Schuurmans, "Maximum Margin Bayesian Networks," Proc. Int'l Conf. Uncertainty in Artificial Intelligence, pp. 233-242, 2005.
2. V. Vapnik, Statistical Learning Theory. Wiley & Sons, 1998.
3. B. Taskar, C. Guestrin, and D. Koller, "Max-Margin Markov Networks," Proc. Advances in Neural Information Processing Systems, 2003.
4. H. Wettig, P. Grü nwald, T. Roos, P. Myllymäki, and H. Tirri, "When Discriminative Learning of Bayesian Network Parameters Is Easy," Proc. Int'l Joint Conf. Artificial Intelligence, pp. 491-496, 2003.
5. T. Roos, H. Wettig, P. Grü nwald, P. Myllymäki, and H. Tirri, "On Discriminative Bayesian Network Classifiers and Logistic Regression," Machine Learning, vol. 59, pp. 267-296, 2005. (Pubitemid 40890480)
6. F. Sha and L. Saul, "Comparison of Large Margin Training to Other Discriminative Methods for Phonetic Recognition by Hidden Markov Models," Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, pp. 313-316, 2007.
7. G. Heigold, T. Deselaers, R. Schlü ter, and H. Ney, "Modified MMI/MPE: A Direct Evaluation of the Margin in Speech Recognition," Proc. Int'l Conf. Machine Learning, pp. 384-391, 2008.
8. R. Collobert, F. Siz, J. Weston, and L. Bottou, "Trading Convexity for Scalability," Proc. Int'l Conf. Machine Learning, pp. 201-208, 2006.
9. C. Bishop, Neural Networks for Pattern Recognition. Oxford Univ. Press, 1995.
10. R. Greiner, X. Su, S. Shen, and W. Zhou, "Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers," Machine Learning, vol. 59, pp. 297-322, 2005. (Pubitemid 40890481)
11. O. Gopalakrishnan, D. Kanevsky, A. Nàdas, and D. Nahamoo, "An Inequality for Rational Functions with Applications to Some Statistical Estimation Problems," IEEE Trans. Information Theory, vol. 37, no. 1, pp. 107-113, Jan. 1991.
12. F. Pernkopf and M. Wohlmayr, "On Discriminative Parameter Learning of Bayesian Network Classifiers," Proc. European Conf. Machine Learning, pp. 221-237, 2009.
13. P. Woodland and D. Povey, "Large Scale Discriminative Training of Hidden Markov Models for Speech Recognition," Computer Speech and Language, vol. 16, pp. 25-47, 2002.
14. R. Schlü ter, W. Macherey, M.B., and H. Ney, "Comparison of Discriminative Training Criteria and Optimization Methods for Speech Recognition," Speech Comm., vol. 34, pp. 287-310, 2001. (Pubitemid 32284868)
15. F. Pernkopf and M. Wohlmayr, "Maximum Margin Bayesian Network Classifiers," technical report, Inst. Signal Processing and Speech Comm., Graz Univ. of Technology, 2010.
16. F. Pernkopf and M. Wohlmayr, "Large Margin Learning of Bayesian Classifiers Based on Gaussian Mixture Models," Proc. European Conf. Machine Learning, pp. 50-66, 2010.
17. L. Lamel, R. Kassel, and S. Seneff, "Speech Database Development: Design and Analysis of the Acoustic-Phonetic Corpus," Proc. US Defense Advanced Research Projects Agency Speech Recognition Workshop, 1986.
18. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
19. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
20. F. Pernkopf and J. Bilmes, "Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers," J. Machine Learning Research, vol. 11, pp. 2323-2360, 2010.
21. N. Friedman, D. Geiger, and M. Goldszmidt, "Bayesian Network Classifiers," Machine Learning, vol. 29, pp. 131-163, 1997. (Pubitemid 127510036)
22. P. Domingos and M. Pazzani, "On the Optimality of the Simple Bayesian Classifier under Zero-One Loss," Machine Learning, vol. 29, nos. 2/3, pp. 103-130, 1997. (Pubitemid 127510035)
23. J. Bilmes, "Dynamic Bayesian Multinets," Proc. 16th Int'l Conf. Uncertainty in Artificial Intelligence, pp. 38-45, 2000.
24. R. Cowell, A. Dawid, S. Lauritzen, and D. Spiegelhalter, Probabilistic Networks and Expert Systems. Springer Verlag, 1999.
25. C. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
26. S. Acid, L. de Campos, and J. Castellano, "Learning Bayesian Network Classifiers: Searching in a Space of Partially Directed Acyclic Graphs," Machine Learning, vol. 59, pp. 213-235, 2005. (Pubitemid 40890478)
27. B. Schölkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2001.
28. P. Huber, "Robust Estimation of a Location Parameter," Annals of Statistics, vol. 53, pp. 73-101, 1964.
29. O. Chapelle, "Training a Support Vector Machine in the Primal," Neural Computation, vol. 19, no. 5, pp. 1155-1178, 2007.
30. W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, Numerical Recipes in C. Cambridge Univ. Press, 1992.
31. T. Cover and J. Thomas, Elements of Information Theory. John Wiley & Sons, 1991.
32. E. Keogh and M. Pazzani, "Learning Augmented Bayesian Classifiers: A Comparison of Distribution-Based and Classification-Based Approaches," Proc. Workshop Artificial Intelligence and Statistics, pp. 225-230, 1999.
33. F. Pernkopf, "Bayesian Network Classifiers versus Selective k-NN Classifier," Pattern Recognition, vol. 38, no. 3, pp. 1-10, 2005.
34. D. Grossman and P. Domingos, "Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood," Proc. Int'l Conf. Machine Lerning, pp. 361-368, 2004. (Pubitemid 40290829)
35. P. Bartlett, M. Jordan, and J. McAuliffe, "Convexity, Classification, and Risk Bounds," J. Am. Statistical Assoc., vol. 101, no. 473, pp. 138-156, 2006. (Pubitemid 43500031)
36. F. Pernkopf and M. Wohlmayr, "Stochastic Margin-Based Structure Learning of Bayesian Network Classifiers," technical report, Laboratory of Signal Processing and Speech Comm., Graz Univ. of Technology, 2011.
37. F. Pernkopf and J. Bilmes, "Order-Based Discriminative Structure Learning for Bayesian Network Classifiers," Proc. Int'l Symp. Artificial Intelligence and Math., 2008.
38. U. Fayyad and K. Irani, "Multi-Interval Discretizaton of Continuous-Valued Attributes for Classification Learning," Proc. Joint Conf. Artificial Intelligence, pp. 1022-1027, 1993.
39. F. Pernkopf, T. Van Pham, and J. Bilmes, "Broad Phonetic Classification Using Discriminative Bayesian Networks," Speech Comm., vol. 143, no. 1, pp. 123-138, 2008.
40. A. Wächter and L. Biegler, "On the Implementation of an Interior-Point Filter Line-Search Algorithm for Large-Scale Nonlinear Programming," Math. Programming, vol. 106, pp. 25-57, 2006. (Pubitemid 41813168)
41. L. Biegler and V. Zavala, "Large-Scale Nonlinear Programming Using IPOPT: An Integrating Framework for Enterprise-Wide Dynamic Optimization," Computers & Chemical Eng., vol. 33, no. 3, pp. 575-582, 2009.
42. P. Amestoy, I. Duff, J.-Y. L'Excellent, and J. Koster, "MUMPS: A General Purpose Distributed Memory Sparse Solver," Proc. Fifth Int'l Workshop Applied Parallel Computing, pp. 122-131, 2000.
43. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge Univ. Press, Mar. 2004.