A comparison of model aggregation methods for regression

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2714, Issue , 2003, Pages 76-83

  Barutçuoǧlu, Zafer ^a   Alpaydin, Ethem ^a  

^a BOGAZICI UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; NEURAL NETWORKS;

ADABOOST ALGORITHM; AGGREGATE MODEL; BASE MODELS; COMPARISON OF MODELS; MACHINE LEARNING MODELS; OVERALL ACCURACIES; REAL-WORLD;

ADAPTIVE BOOSTING;

EID: 21144455920     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Review

References (12)

1. Breiman, L., "Bagging Predictors", Machine Learning, Vol. 24, No. 2, pp. 123-140, 1996.
2. Freund, Y. and R. E. Schapire, "A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting", European Conf. on Computational Learning Theory, pp. 23-37, 1995.
3. Freund, Y. and R. E. Schapire, "Experiments with a New Boosting Algorithm", International Conf. on Machine Learning, pp. 148-156, 1996.
4. Ridgeway, G., D. Madigan and T. Richardson, "Boosting methodology for regression problems", Proc. of Artificial Intelligence and Statistics, pp. 152-161, 1999.
5. Drucker, H., "Improving regressors using boosting techniques", Proc. 14th International Conf. on Machine Learning, pp. 107-115, Morgan Kaufmann, San Francisco, CA, 1997.
6. Zemcl, R. S. and T. Pitassi, "A Gradient-Based Boosting Algorithm for Regression Problems", Adv. in Neural Information Processing Systems, Vol. 13, 2001.
7. Friedman, J. H., Greedy Function Approximation: a Gradient Boosting Machine, Tech. Rep. 7, Stanford University, Dept. of Statistics, 1999.
8. Duffy, N. and D. Helmbold, "Leveraging for Regression", Proc. 13th Annual Conf. on Computational Learning Theory, pp. 208-219, Morgan Kaufmann, San Francisco, CA, 2000.
9. Rätsch, G., M. Warmuth, S. Mika, T. Onoda, S. Lemm and K.-R. Müller, "Barrier Boosting", Proc. 13th Annual Conference on Computational Learning Theory, 2000.
10. Alpaydin, E., "Combined 5x2cv F Test for Comparing Supervised Classification Learning Algorithms", Neural Computational, 11, No. 8, pp. 1885-1992, 1999.
11. ∼mlearn/MLRepository.html.
12. Hosmer, D. and S. Lemeshow, Applied Logistic Regression, John Wiley & Sons Inc., 2nd edn., 2000.