Ensembles of region based classifiers

CIT 2007: 7th IEEE International Conference on Computer and Information Technology

Volumn , Issue , 2007, Pages 41-46

  Choi, Sungha ^a   Lee, Byungwoo ^b   Yang, Jihoon ^b  

^a LG Electronics Inc   (South Korea)

^b Sogang University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; DATA STRUCTURES; PATTERN RECOGNITION;

CLASSIFIERS (INFORMATION); FEATURE SPACE;

LEARNING SYSTEMS;

EID: 38049039284     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIT.2007.4385054     Document Type: Conference Paper

References (22)

1. Bauer, E., & Kohavi, R., "An Empirical Comparison of Voting Classification Algorithm: Bagging, Boosting, and Variants", Machine Learning, 36(1-2), 1999, pp. 105-142.
2. Blake, C., & Merz, C., UCI Repository of Machine Learning Database, http://www.ics.uci.edu/~mlearn/MLRepository.html, 1998.
3. Breiman, L., "Bias, Variance, and Arcing Classifiers", Technical Report TR, 460, UC Berkeley, 1996.
4. Breiman, L., "Bagging Predictors. Machine Learning", 24(2), 1996, pp. 123-140.
5. Buciu, I., Kotropoulos, C., & Pitas, I., "Combining Support Vector Machines for Accurate Face Detection", In Pr oc. of ICIP'01, 1, 2001, pp. 1054-1057.
6. Dietterich, T., "An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization", Machine Learning, 40(2), 2000, pp. 139-157.
7. Dietterich, T., "Ensemble method in Machine learning", In J. Kittler and F. Roli (Ed.), First International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, 2000, pp. 1-15.
8. Dietterich, T., "Ensemble Learning", In The Handbook of Brain Theory and Neural Networks, Second edition. The MIT Press, 2002, pp. 405-408.
9. Evgeniou, T., Perez-Breva, L., Pontil, M., & Poggio, T., "Bound on the Generalization Performance of Kernel Machine Ensembles", In Proc. of the 17th International Conference on Machine Learning, 2000, pp. 271-278.
10. Freund, Y., & Schapire, R., "Experiments with a new boosting algorithm", In Proc. of the 13th International Conference on Machine Learning, 1996, pp. 148-156.
11. Freund, Y., & Schapire, R., "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting", Journal of Computer and System Science, 55, 1997, pp. 119-139.
12. Friedman, J., Hastie, T., & Tibshirani, R., "Additive Logistic Regression: a Statistical View of Boosting", Annals of Statistics, 28(2), 2000, pp. 337-374.
13. Hansen, L., & Salamon, P., "Neural Network Ensembles", IEEE Transaction on Pattern Analysis and Machine Intelligence, 12, 1990, pp. 993-1001.
14. Kim, H., "Construction Support Vector Machine Ensemble", Pattern Recognition, 36,2003, pp. 2757-2767.
15. Kuncheva, L., & Whitaker, C., "Measures of diversity in classifier ensembles", Machine Learning, 51, 2003, pp. 181-207.
16. Opitz, D., & Maclin, R., "Popular Ensemble Methods: An Empirical Study", Journal of Artificial Intelligence Research, 11, 1999, pp. 169-198.
17. Platt, J., "Fast Training of Support Vector Machines using Sequential Minimal Optimization", chapter 12, The MIT Press, 1999, pp. 185-208.
18. Quinlan, J., "Induction of Decision Tree", Machine Learning, 1(1), 1986, pp. 81-106
19. Quinlan, J., "Bagging, Boosting, and C4.5", In Proc. of the Thirteenth National Conference on Artificial Intelligence, 1996, pp. 725-730.
20. Quinlan, J., C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
21. Valentini, G., Muselli, M., & Ruffino, F., "Bagged Ensembles of SVMs for Gene Expression Data Analysis", The IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2003, pp. 1844-1849.
22. Witten, I., & Frank, E., Data Mining: Practical Machine Learning Tools and Techniques with Java Implementation, Second edition. Morgan Kaufmann, 2005.