Adaptive random forest how many "experts" to ask before making a decision?

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2011, Pages 1377-1384

  Schwing, Alexander G ^a   Zach, Christopher ^a   Zheng, Yefeng ^b   Pollefeys, Marc ^a  

^a ETH ZURICH   (Switzerland)

^b SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; ECONOMIC AND SOCIAL EFFECTS;

CLASSIFICATION PROCESS; CONFIDENCE INTERVAL; CONJUGATE PRIOR; HEURISTIC PARAMETERS; MULTINOMIAL DISTRIBUTIONS; RANDOM FOREST CLASSIFICATION; RANDOM FORESTS; STATISTICAL FORMULATION;

PATTERN RECOGNITION;

EID: 80052899904     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995684     Document Type: Conference Paper

References (30)

1. A. Asuncion and D. Newman UCI Machine Learning Repository. http://www.ics.uci.edu/~mlearn/MLRepository.html, 2007. 1381
2. B. J. R. Bailey. Large sample simultaneous confidence intervals for the multinomial probabilities based on transformations of the cell frequencies. Technometrics, 22:583-589, 1980. 1381
3. L. Bourdev and J. Brandt. Robust object detection via soft cascade. In Proc. CVPR, volume 2, pages 236-243, 2005. 1379
4. L. Breiman. Random Forests. Machine Learning, 45(1):5-32, 2001. 1377, 1378, 1379, 1381, 1382
5. L. Breiman, J. Friedman, C. J. Stone, and R. Olshen. Classification and Regression Trees. Chapman and Hall, 1984. 1378
6. D. Chafái and D. Concordet. Confidence regions for the multinomial parameter with small sample size. J. of the American Stat. Association, 104(487):1071-1079, 2009. 1379, 1381
7. J. Šochman and J. Matas. WaldBoost - Learning for Time Constrained Sequential Detection. In Proc. CVPR, pages 150-157, 2005. 1379
8. O. Chum and J. Matas. Optimal Randomized RANSAC. IEEE Trans. Pattern Analysis & Machine Intelligence, 30(8):1472-1482, 2008. 1379
9. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proc. CVPR, pages 886-893, 2005. 1377
10. M. A. Fischler and R. C. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications ACM, 24(6):381-395, 1981. 1379
11. Y. Freund and R. E. Schapire. Experiments with a new boosting algorithm. In Proc. Int'l Conf. on Machine Learning (ICML), pages 148-156, 1996. 1379
12. A. Genz and K.-S. Kwong. Numerical evaluation of singular multivariate normal distributions. J. of Stat. Computation and Simulation, 68:1-21, 1999. 1381
13. P. Geurts, D. Ernst, and L. Wehenkel. Extremely randomized trees. Machine Learning, 36(1):3-42, 2006. 1377
14. 2 tests in a Markov chain. Annals of Math. Statistics, 30:56-74, 1963. 1381
15. L. Goodman. On simultaneous confidence intervals for multinomial proportions. Technometrics, 7:247-254, 1965. 1381
16. Z. Govindarajulu. Sequential Statistics. World Scientific Publishing, 2004. 1380
17. nd edition, 2009. 1378, 1381
18. K.-S. Kwong and B. Iglewicz. On singular multivariate normal distribution and its applications. Computational Statistics & Data Analysis, 22(3):271-285, 1996. 1381
19. V. Lempitsky, M. Verhoek, A. Noble, and A. Blake. Random forest classification for automatic delineation of myocardium in real-time 3D echocardiography. In Proc. Int'l Conf. on Functional Imaging and Modeling of the Heart (FIMH), pages 447-456, 2009. 1381, 1382, 1383
20. D. G. Lowe. Object recognition from local scale-invariant features. In Proc. ICCV, pages 1150-1157, 1999. 1377
21. M. Özuysal, P. Fua, and V. Lepetit. Fast keypoint recognition in ten lines of code. In Proc. CVPR, 2007. 1377, 1379
22. C. Quesenberry and D. Hurst. Large sample simultaneous confidence intervals for multinomial proportions. Technometrics, 6:191-195, 1964. 1381
23. M. Reynolds, J. Doboš, L. Peel, T. Weyrich, and G. J. Brostow. Capturing time-of-flight data with confidence. In Proc. CVPR, 2011. 1377
24. R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistic, 26(5):1651-1686, 1998. 1379
25. A. G. Schwing. Adaptive Random Forest - How many "experts" to ask before making a decision? Supplementary Material. http://www.alexander- schwing.de, 2011. 1382, 1383
26. T. Sharp. Implementing decision trees and forests on a GPU. In Proc. ECCV, pages 595-608, 2008. 1379
27. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proc. CVPR, pages 511-518, 2001. 1377
28. A. Wald. Sequential Analysis. Dover, 1947. 1379
29. Z. Yi, A. Criminisi, J. Shotton, and A. Blake. Discriminative, semantic segmentation of brain tissue in MR images. In Proc. Int'l Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009. 1378
30. Z. Zhou, J. Wu, and W. Tang. Ensembling neural networks: many could be better than all. Artificial Intelligence, 137(1-2):239-263, 2002. 1379