Randomizing outputs to increase prediction accuracy

Machine Learning

Volumn 40, Issue 3, 2000, Pages 229-242

  Breiman, Leo ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ERROR ANALYSIS; LEARNING ALGORITHMS; MONTE CARLO METHODS; PERTURBATION TECHNIQUES; PROBLEM SOLVING; RANDOM PROCESSES; REGRESSION ANALYSIS; SET THEORY; TREES (MATHEMATICS);

ADABOOST ALGORITHM; OUTPUT VARIABILITY; PERTURBED TRAINING SETS; RANDOMIZING OUTPUTS;

LEARNING SYSTEMS;

EID: 0034276320     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007682208299     Document Type: Article

References (14)

1. An, G. (1996). The effects of adding noise during backpropagation training on generalization performance. Neural Computation, 6, 643-674.
2. Breiman, L. (1996a). Bagging predictors. Machine Learning, 26(2), 123-140.
3. Breiman, L. (1996b). The heuristics of instability in model selection. Annals of Statistics, 24, 2350-2383.
4. Breiman, L. (1997). Prediction games and arcing algorithms. Technical Report 504, Statistics Department, University of California at Berkeley. Available at www.stat.berkeley.edu
5. Breiman, L. (1998a). Arcing classifiers (with discussion). Annals of Statistics, 26, 801-849.
6. Breiman, L. (1998b). Half and half bagging and hard boundary points. Technical Report 534, Statistics Dept. Univ. of Calif. at Berkeley.
7. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Chapman and Hall.
8. Dietterich, T. (1998). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization. Machine Learning, 1-22.
9. Freund, Y. & Schapire, R. (1997). A decision-theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139.
10. Freund, Y. & Schapire, R. (1996). Experiments with a new boosting algorithm. In Machine Learning: Proceedings of the Thirteenth International Conference (pp. 148-156).
11. Freund, Y. & Schapire, R. (in press). Discussion of "Arcing Classifiers" by L. Breiman. Annals of Statistics.
12. Friedman, J. (1991). Multivariate adaptive regression splines (with discussion). Annals of Statistics, 19, 1-141.
13. Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4, 1-58.
14. Grossamn, T. & Lapedes, A. (1993). Use of bad training data for better predictions. NIPS, 6, 343-350.