Boosting Decision Trees

NIPS 1995: Proceedings of the 8th International Conference on Neural Information Processing Systems

Volumn , Issue , 1995, Pages 479-485

  Drucker, Harris ^a   Cortes, Corinna ^a  

^a LUCENT TECHNOLOGIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; ITERATIVE METHODS; OPTICAL CHARACTER RECOGNITION; PROBABILITY DISTRIBUTIONS;

BOOSTING ALGORITHM; BOOSTING ENSEMBLES; C4.5 ALGORITHM; CLASSIFIEDS; DATA MINING PROBLEMS; FEATURE SPACE; HIGH PROBABILITY; KNOWLEDGE DISCOVERY AND DATA MININGS; PERFORMANCE; PROBABILITY: DISTRIBUTIONS;

DECISION TREES;

EID: 85156217048     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackei, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik (1994), "Comparison of Classifier Methods: A Case Study in Handwritten Digit Recognition", 1994 International Conference on Pattern Recognition, Jerusalem.
2. L. Breiman, J. Friedman, R. A. Olshen, and C. J. Stone (1984), Classification and Regression Trees, Chapman and Hall.
3. L. Breiman, "Bagging Predictors", Technical Report No. 421, Department of Statistics University of California, Berkeley, California 94720, September 1994.
4. H. Drucker (1994), C. Cortes, LD Jackel, Y. LeCun "Boosting and Other Ensemble Methods", Neural Computation, vol 6, no. 6, pp. 1287-1299.
5. H. Drucker, R. E. Schapire, and P. Simard (1993) "Boosting Performance in Neural Networks", International Journal of Pattern Recognition and Artificial Intelligence, Vol 7. No 4, pp. 705-719.
6. Y. Freund (1990), "Boosting a Weak Learning Algorithm by Majority", Proceedings of the Third Workshop on Computational Learning Theory, Morgan-Kaufmann, 202-216.
7. Y. Freund and R. E. Schapire (1995), "A decision-theoretic generalization of on-line learning and an application to boosting", Proceeding of the Second European Conference on Computational Learning.
8. T. K. Ho (1992), A theory of Multiple Classifier Systems and Its Applications to Visual Word Recognition, Doctoral Dissertation, Department of Computer Science, SUNY at Buffalo.
9. S. W. Kwok and C. Carter (1990), "Multiple Decision Trees", Uncertainty in Artificial Intelligence 4, R. D. Shachter, T. S. Levitt, L. N. Kanal, J. F Lern mer (eds) Elsevier Science Publishers.
10. J. R. Quinlan (1993), C4.5: Programs For Machine Learning, Morgan Kauffman.
11. J. Mingers (1989), "An Empirical Comparison of Pruning Methods for Decision Tree Induction", Machine Learning, 4:227-243.
12. R. E. Schapire (1990), The strength of weak learnability, Machine Learning, 5 (2): 197-227.