Boosting neural networks

Neural Computation

Volumn 12, Issue 8, 2000, Pages 1869-1887

  Schwenk, H ^a   Bengio, Y ^b  

^a CNRS   (France)

^b UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; HANDWRITING;

ALGORITHMS; HANDWRITING; NEURAL NETWORKS (COMPUTER);

EID: 0034243471     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976600300015178     Document Type: Article

References (38)

1. Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36, 105-139.
2. Bourrely, J. (1989). Parallelization of a neural learning algorithm on a hypercube. In Hypercube and distributed computers (pp. 219-229). Amsterdam: Elsievier Science, North-Holland.
3. Breiman, L. (1994). Bagging predictors. Machine Learning, 24(2), 123-140.
4. Breiman, L. (1996). Bias, variance, and arcing classifiers (Tech. Rep. No. 460). Berkeley: Statistics Department, University of California at Berkeley.
5. Breiman, L. (1997a). Arcing the edge (Tech. Rep. No. 486). Berkeley: Statistics Department, University of California at Berkeley.
6. Breiman, L. (1997b). Prediction games and arcing classifiers (Tech. Rep. No. 504). Berkeley: Statistics Department, University of California at Berkeley.
7. Breiman, L. (1998). Arcing classifiers. Annuals of Statistics, 26(3), 801-849.
8. Dietterich, T. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7), 1895-1924.
9. Dietterich, T. G. (forthcoming). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning. Available online at: ftp://ftp.cs.orst.edu/pub/ tgd/papers/tr-randomized-c4.ps.gz.
10. Drucker, H., & Cortes, C. (1996). Boosting decision trees. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in neural information processing systems (pp. 479-485). Cambridge, MA: MIT Press.
11. Feng, C., Sutherland, A., King, R., Muggleton, S., & Henery, R. (1993). Comparison of machine learning classifiers to statistics and neural networks. In Proceedings of the Fourth International Workshop on Artificial Intelligence and Statistics (pp. 41-52).
12. Freund, Y. (1995). Boosting a weak learning algorithm by majority. Information and Computation, 121(2), 256-285.
13. Freund, Y., & Schapire, R. E. (1996a). Experiments with a new boosting algorithm. In Machine Learning: Proceedings of Thirteenth International Conference (pp. 148-156).
14. Freund, Y., & Schapire, R. E. (1996b). Game theory, on-line prediction and boosting. In Proceedings of the Ninth Annual Conference on Computational Learning Theory (pp. 325-332).
15. Freund, Y., & Schapire, R. E. (1997). A decision theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Science, 55(1), 119-139.
16. Freund, Y., & Schapire, R. E. (1999). Adaptive game playing using multiplicative weights. Games and Economic Behavior, 29, 79-103.
17. Friedman, J., Hastie, T., & Tibshirani, R. (1998). Additive logistic regression: A statistical view of boosting (Tech. Rep.). Stanford: Department of Statistics, Stanford University.
18. Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4(1), 1-58.
19. Grove, A. J., & Schuurmans, D. (1998). Boosting in the limit: Maximizing the margin of learned ensembles. In Proceedings of the Fifteenth National Conference on Artificial Intelligence.
20. Kohavi, R., & Wolpert, D. H. (1996). Bias plus variance decomposition for zero-one loss functions. In Machine Learning: Proceedings of Thirteenth International Conference (pp. 275-283).
21. Kong, E. B., & Dietterich, T. G. (1995). Error-correcting output coding corrects bias and variance. In Machine Learning: Proceedings of Twelfth International Conference (pp. 313-321).
22. Krogh, A., & Vedelsby, J. (1995). Neural network ensembles, cross validation and active learning. In G. Tesauro, D. S. Touretzky, & T. K. Leen, (Eds.), Advances in neural information processing systems, 7, (pp. 231-238). Cambridge, MA: MIT Press.
23. Maclin, R., & Opitz, D. (1997). An empirical evaluation of bagging and boosting. In Proceedings of the fourteenth National Conference on Artificial Intelligence (pp. 546-551).
24. Mason, L., & Baxter, P. B. J. (1999). Direct optimization of margins improves generalization in combined classifiers. In M. S. Kearns, S. Solla, & D. Cohn (Eds.), Advances in neural information processing systems, 11, Cambridge, MA: MIT Press.
25. Moller, M. (1992). Supervised learning on large redundant training sets. In Neural networks for signal processing 2. New York: IEEE Press.
26. Moller, M. (1993). Efficient training of feed-forward neural networks. Unpublished doctoral dissertation, Aarhus University, Aarhus, Denmark.
27. Perrone, M. P. (1993). Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization. Unpublished doctoral dissertation, Brown University, Providence, RI.
28. Perrone, M. P. (1994). Putting it all together: Methods for combining neural networks. In J. D. Cowan, G. Tesauro, & J. Alspector, (Eds.), Advances in neural information processing systems, 6, (pp. 1188-1189). San Mateo, CA: Morgan Kaufmann.
29. Quinlan, J. R. (1996). Bagging, boosting and C4.5. In Machine Learning: Proceedings of the Fourteenth International Conference (pp. 725-730).
30. Rätsch, G., Onoda, T., & Müller, K.-R. (1998). Soft margins for AdaBoost (Tech. Rep. No. NC-TR-1998-021). Egham, Surrey: Royal Holloway College, University of London.
31. Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.
32. Schapire, R. E. (1999). Theoretical views of boosting. In Computational Learning Theory: Fourth European Conference, EuroCOLT (pp. 1-10).
33. Schapire, R. E., Freund, Y., Bartlett, P., & Lee, W. S. (1997). Boosting the margin: A new explanation for the effectiveness of voting methods. In Machine Learning: Proceedings of Fourteenth International Conference (pp. 322-330).
34. Schapire, R. E., & Singer, Y. (1998). Improved boosting algorithms using confidence rated predictions. In Proceedings of the 11th Annual Conference on Computational Learning Theory.
35. Schwenk, H., & Bengio, Y. (1997). Adaboosting neural networks: Application to on-line character recognition. In International Conference on Artificial Neural Networks (pp. 967-972). Berlin: Springer-Verlag.
36. Schwenk, H., & Bengio, Y. (1998). Training methods for adaptive boosting of neural networks. In M. I. Jordan, M. J. Kearns, & S. A. Solla, (Eds.), Advances in neural information processing systems, 10 (pp. 647-653). Cambridge, MA: MIT Press.
37. Schwenk, H., & Milgram, M. (1996). Constraint tangent distance for online character recognition. In International Conference on Pattern Recognition (pp. D:520-524). New York: IEEE Computer Society Press.
38. Tibshirani, R. (1996). Bias, variance and prediction error for classification rules (Tech. Rep.). Toronto: Department of Statistics, University of Toronto.