Creating ensemble of diverse maximum entropy models

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2012, Pages 4845-4848

  Audhkhasi, Kartik ^a   Sethy, Abhinav ^b   Ramabhadran, Bhuvana ^b   Narayanan, Shrikanth S ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

classifier diversity; Maximum entropy model

Indexed keywords

CLASSIFICATION PERFORMANCE; CLASSIFIER ENSEMBLES; CONVENTIONAL METHODS; CROSS CORRELATIONS; EMOTION RECOGNITION; KL-DIVERGENCE; LANGUAGE PROCESSING; MAXENT MODELS; MAXIMUM ENTROPY; MAXIMUM ENTROPY MODELS; NAMED ENTITY RECOGNITION; OBJECTIVE FUNCTIONS; POSTERIOR DISTRIBUTIONS; REFERENCE MODELS;

SIGNAL PROCESSING;

MAXIMUM ENTROPY METHODS;

EID: 84867593677     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2012.6289004     Document Type: Conference Paper

References (18)

1. J. Bennett and S. Lanning, "The Netflix Prize," in Proceedings of KDD Cup and Workshop, 2007, pp. 35-38.
2. A. Niculescu-Mizil et al., "Winning the KDD Cup Orange Challenge with ensemble selection," in KDD Cup and Workshop in conjunction with KDD, 2009.
3. G. Saon et al., "The IBM 2008 GALE Arabic speech transcription system," in Proc. ICASSP, 2010, pp. 4378-4381.
4. T. Dietterich, "Ensemble methods in machine learning," Multiple classifier systems, pp. 1-15, 2000.
5. L. Breiman, "Bagging predictors," Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
6. Y. Freund and R. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," in Computational learning theory. Springer, 1995, pp. 23-37.
7. L.I. Kuncheva, Combining pattern classifiers: methods and algorithms, Wiley-Interscience, 2004.
8. N. Ueda and R. Nakano, "Generalization error of ensemble estimators," in Proc. ICNN, 1996, vol. 1, pp. 90-95.
9. K. Tumer and J. Ghosh, "Analysis of decision boundaries in linearly combined neural classifiers," Pattern Recognition, vol. 29, no. 2, pp. 341-348, 1996.
10. Y. Liu and X. Yao, "Ensemble learning via negative correlation," Neural Networks, , no. 10, pp. 1399-1404, 1999.
11. P. Melville and R.J. Mooney, "Constructing diverse classifier ensembles using artificial training examples," in Proc. IJCAI, 2003, pp. 505-510.
12. D.C. Liu and J. Nocedal, "On the limited memory BFGS method for large scale optimization," Mathematical programming, vol. 45, no. 1, pp. 503-528, 1989.
13. L.I. Kuncheva and C.J. Whitaker, "Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy," Machine Learning, vol. 51, no. 2, pp. 181-207, 2003.
14. E.F. Tjong Kim Sang and F. De Meulder, "Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition," in Proc. CoNLL, 2003, pp. 142-147.
15. J.R. Finkel, T. Grenager, and C.Manning, "Incorporating nonlocal information into information extraction systems by Gibbs sampling," in Proc. ACL, 2005, pp. 363-370.
16. B. Zhang, A. Sethy, T. N. Sainath, and B. Ramabhadran, "Application specific loss minimization using gradient boosting," in Proc. ICASSP, 2011, pp. 4880-4883.
17. C. Busso et al., "IEMOCAP: Interactive Emotional Dyadic Motion Capture Database," Language resources and evaluation, vol. 42, no. 4, pp. 335-359, 2008.
18. F. Eyben, M. Wollmer, and B. Schuller, "OpenSMILE: the munich versatile and fast open-source audio feature extractor," in Proc. Intl. Conf. on Multimedia, 2010, pp. 1459-1462.