Generalized rules for combination and joint training of classifiers

Pattern Analysis and Applications

Volumn 6, Issue 3, 2003, Pages 201-211

  Bilmes, J A ^a   Kirchhoff, K ^a  

^a University of Washington   (United States)

Author keywords

Classifier combination; Combination rules; Joint classifier training; Mixtures of experts; Neural network ensembles; Neural networks; Products of experts; Speech recognition

Indexed keywords

EID: 0346331967     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10044-002-0188-0     Document Type: Article

References (33)

1. Kittler J, Hataf M, Duin RPW and Matas J, On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998; 20(3): 226-239
2. Jacobs RA, Methods for combining experts' probability assessments. Neural Computation 1995; 7: 867-888
3. Krogh A and Vedelsby J, Neural network ensembles, cross validation, and active learning. Advances in Neural Information Processing Systems 7. MIT Press, 1995
4. Perrone MP and Cooper LN, When networks disagree: ensemble methods for hybrid neural networks. In RJ Mammone, editor, Neural networks for speech and image processing. Chapman & Hall, 1993; 126-142
5. Wolpert DH, Stacked generalization. Neural Networks 1992; 5: 241-259
6. Sharkey AJC, Multi-net systems. In Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems. Springer, 1999, pp 3-30
7. Bishop C, Neural networks for pattern recognition. Clarendon Press, Oxford, 1995
8. Turner K and Ghosh J, Analysis of decision boundaries in linearly combined neural classifier. Pattern Recognition 1996; 29: 341-348
9. Sharkey AJC, Sharkey NE and G Chandroth GO, Neural nets and diversity. In Proceedings 14th international conference on Computer safety, reliability and security, 1995; 375-389
10. Ho T-K, Hull JJ and Srihari SN, Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 1994; 16(1): 66-75
11. Hashem S, Optimal linear combinations of neural networks. Neural Networks 1997; 10(4): 599-614
12. Rogova G, Combining the results of several neural network classifiers. Neural Networks 1994; 7: 777-781
13. Lam L and Suen CY, Optimal combinations of pattern classifiers. Pattern Recognition Letters 1995; 16: 945-954
14. Bilmes JA and Kirchhoff K, Directed graphical models of classifier combination: Application to phone recognition. In Proceedings international conference on spoken language processing, Beijing, China, 2000
15. Bourlard H and Morgan N, Connectionist speech recognition: a hybrid approach. Kluwer Academic, 1994
16. Kingsbury BED and Morgan N, Recognizing reverberant speech with RASTAPLP. In Proceedings ICASSP-97, 1997
17. Halberstadt AK and Glass JR, Heterogeneous measurements and multiple classifiers for speech recognition. In Proceedings international conference on spoken language processing, 1998; 995-998
18. Kirchhoff K, Combining acoustic and articulatory information for speech recognition in noisy and reverberant environments. In Proceedings international Conference on spoken language processing, 1998
19. Mirghafori N and Morgan N, Combining connectionist multi-band and full-band probability streams for speech recognition of natural numbers. In Proceedings international conference on spoken language processing, 1998; 743-746
20. McMahon P, Court P and Vaseghi S, Discriminative weighting of multi-resolution sub-band cepstral features for speech recognition. In Proceedings international conference on spoken language processing, 1998; 1055-1058
21. Cole RA, Noel M, Lander T, Durham T, New telephone speech corpora at CLSU. In European conference on speech communication and technology (Eurospeech), 1995; 821-824
22. Cover TM and Thomas JA, Elements of information theory. Wiley, 1991
23. Kirchhoff K and Bilmes J, Dynamic classifier combination in hybrid speech recognition systems using utterance-level confidence values. Proceedings international conference on acoustic, speech and signal processing, 1999; 693-696
24. Hinton GE, Training products of experts by minimizing contrastive divergence. Technical report, Gatsby Computational Neuroscience Unit, 2000
25. Bilmes J, Natural statistic models for automatic speech recognition. PhD thesis, UC Berkeley, 1999
26. Bilmes JA, Dynamic Bayesian Multinets. In Proceedings 16th conference on uncertainty in artificial intelligence. Morgan Kaufmann, 2000
27. Hermansky H and Morgan N, RASTA processing of speech. IEEE Transactions on Speech and Audio Processing 1994; 2(4): 578-589
28. Breiman L, Bagging predictors. Machine Learning 1996; 26: 123-140
29. Schapire RE, The strength of weak learnability. Machine Learning 1990; 5: 197-227
30. Freund Y and Schapire R, Experiments with a new boosting algorithm. In Proceedings 13th international conference on machine learning 1996; 148-156
31. Schwenk H, Using boosting to improve a hybrid HMM/neural network speech recognizer. In International conference on acoustics, speech and signal processing, Phoenix, AZ, 1999; 1009-1012
32. Rosen B, Ensemble learning using decorrelated neural networks. Connection Science (Special Issue on Combining Artificial Neural Nets: Ensemble Approaches) 1996; 8(3-4): 373-384
33. Opitz D and Maclin R, Popular ensemble methods: an empirical study. Journal of Artificial Intelligence Research 1999; 11: 169-198