A sequential scheduling approach to combining multiple object classifiers using cross-entropy

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

Volumn 2709, Issue , 2003, Pages 135-145

  Magee, Derek ^a  

^a UNIVERSITY OF LEEDS   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BOOSTING; BAYESIAN NETWORKS;

BAYESIAN APPROACHES; BINARY CLASSIFIERS; CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; COMPUTATIONAL SAVINGS; MULTICLASS CLASSIFICATION PROBLEMS; MULTIPLE CLASSIFIERS; MULTIPLE OBJECTS;

CLASSIFICATION (OF INFORMATION);

EID: 35248863970     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44938-8_14     Document Type: Article

References (15)

1. Schapire, R.: The boosting approach to machine learning: An overview. In: Proc. MSRI Workshop on Nonlinear Estimation and Classification. (2002)
2. Schapire, R., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3) (1999) 297-336
3. Breiman, L.: Bagging predictors. Machine Learning 24 (1996) 123-140
4. Jordan, M.: Hierarchical mixtures of experts and the EM algorithm. Neural Computation 6 (1994) 181-214
5. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. IEEE C.V.P.R. (2001) 1.511-1.518
6. Merz, C.: Dynamical selection of learning algorithms. In Fisher, D., Lenz, H., eds.: Learning from Data: Artificial Intelligence and Statistics, 5. (1996)
7. Woods, K., Kegelmeyer, W., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans, on P.A.M.I. 19 (1997) 405-410
8. Kuncheva, L.: 'change-glasses' approach in pattern recognition. Pattern Recognition Letters 14 (1993) 619-623
9. Parhami, B.: Voting algorithms. IEEE Trans, on Reliability 43(4) (1994) 617-629
10. Alkoot, F., Kitler, J.: Improving performance of the product fusion strategy. In: Proc. International Conference on Pattern Recognition. (2000)
11. Schiele, B.: How many classifiers do I need? In: Proc. I.C.P.R. (2002) 176-179
12. Merz, C.: Using correspondance analysis to combine classifiers. Machine Learning 0 (1997) 1-26
13. Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning 36 (1999) 105-142
14. Dempster, A., N. Laird, D.R.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B 39 (1977) 1-38
15. Magee, D.: Tracking multiple vehicles using foreground, background and motion models. In: Proc. ECCV: Statistical Methods in Video Processing. (2002) 7-12