Decision jungles: Compact and rich models for classification

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Shotton, Jamie ^a   Nowozin, Sebastian ^a   Sharp, Toby ^a   Winn, John ^a   Kohli, Pushmeet ^a   Criminisi, Antonio ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; LEARNING ALGORITHMS; MERGING;

DECISION DIRECTED ACYCLIC GRAPHS; DISCRIMINATIVE MODELS; EMBEDDED PROCESSORS; EXPONENTIAL GROWTH; FUNDAMENTAL LIMITATIONS; MERGING ALGORITHMS; OBJECTIVE FUNCTIONS; RANDOMIZED DECISIONS;

FORESTRY;

ALGORITHMS; DECISION MAKING; FORESTRY; TREES;

EID: 84899026814     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. Y. Amit and D. Geman. Randomized inquiries about shape; an application to handwritten digit recognition. Technical Report 401, Dept. of Statistics, University of Chicago, IL, Nov 1994.
2. A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh. Clustering with Bregman divergences. Journal of Machine Learning Research, 6:1705-1749, Oct. 2005.
3. D. Benbouzid, R. Busa-Fekete, and B. Kégl. Fast classification using sparse decision DAGs. In Proc. Intl Conf. on Machine Learning (ICML), New York, NY, USA, 2012. ACM.
4. K. P. Bennett, N. Cristianini, J. Shawe-Taylor, and D. Wu. Enlarging the margins in perceptron decision trees. Machine Learning, 41(3):295-313, 2000.
5. L. Breiman. Random forests. Machine Learning, 45(1), 2001.
6. L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen. Classification and Regression Trees. Chapman and Hall/CRC, 1984.
7. H. Chipman, E. I. George, and R. E. Mcculloch. Bayesian CART model search. Journal of the American Statistical Association, 93:935-960, 1997.
8. P. Chou. Optimal partitioning for classification and regression trees. IEEE Trans. PAMI, 13(4), 1991.
9. A. Criminisi and J. Shotton. Decision Forests for Computer Vision and Medical Image Analysis. Springer, 2013.
10. T. Elomaa and M. Kääriäinen. On the practice of branching program boosting. In European Conf. on Machine Learning (ECML), 2001.
11. M. Everingham, L. van Gool, C. Williams, J. Winn, and A. Zisserman. The Pascal Visual Object Classes (VOC) Challenge. http://www.pascal-network.org/ challenges/VOC/.
12. S. Gould, R. Fulton, and D. Koller. Decomposing a scene into geometric and semantically consistent regions. In Proc. IEEE ICCV, 2009.
13. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer, 2001.
14. E. B. Hunt, J. Marin, and P. T. Stone. Experiments in Induction. Academic Press, New York, 1966.
15. L. Hyafil and R. L. Rivest. Constructing optimal binary decision trees is NP-complete. Information Processing Letters, 5(1):15-17, 1976.
16. B. Kijsirikul, N. Ussivakul, and S. Meknavin. Adaptive directed acyclic graphs for multiclass classification. In Pacific Rim Intl Conference on Artificial Intelligence (PRICAI), 2002.
17. R. Kohavi and C.-H. Li. Oblivious decision trees, graphs, and top-down pruning. In Intl Joint Conf. on Artifical Intelligence (IJCAI), 1995.
18. P. Kontschieder, P. Kohli, J. Shotton, and A. Criminisi. GeoF: Geodesic forests for learning coupled predictors. In Proc. IEEE CVPR, 2013.
19. V. Lepetit and P. Fua. Keypoint recognition using randomized trees. IEEE Trans. PAMI, 2006.
20. J. Mahoney and R. J. Mooney. Initializing ID5R with a domain theory: some negative results. Technical Report 91-154, Dept. of Computer Science, University of Texas, Austin, TX, 1991.
21. K. V. S. Murthy and S. L. Salzberg. On growing better decision trees from data. PhD thesis, John Hopkins University, 1995.
22. D. Newman, S. Hettich, C. Blake, and C. Merz. UCI repository of machine learning databases. Technical Report 28, University of California, Irvine, Department of Information and Computer Science, 1998.
23. A. L. Oliveira and A. Sangiovanni-Vincentelli. Using the minimum description length principle to infer reduced ordered decision graphs. Machine Learning, 12, 1995.
24. J. J. Oliver. Decision graphs - an extension of decision trees. Technical Report 92/173, Dept. of Computer Science, Monash University, Victoria, Australia, 1992.
25. A. H. Peterson and T. R. Martinez. Reducing decision trees ensemble size using parallel decision DAGs. Intl Journ. on Artificial Intelligence Tools, 18(4), 2009.
26. J. C. Platt, N. Cristianini, and J. Shawe-Taylor. Large margin DAGs for multiclass classification. In Proc. NIPS, pages 547-553, 2000.
27. J. R. Quinlan. Induction of decision trees. Machine Learning, 1986.
28. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
29. J. Shotton, R. Girshick, A. Fitzgibbon, T. Sharp, M. Cook, M. Finocchio, R. Moore, P. Kohli, A. Criminisi, A. Kipman, and A. Blake. Efficient human pose estimation from single depth images. IEEE Trans. PAMI, 2013.