Efficient non-greedy optimization of decision trees

Advances in Neural Information Processing Systems

Volumn 2015-January, Issue , 2015, Pages 1729-1737

  Norouzi, Mohammad ^a,c   Collins, Maxwell D ^b,c   Johnson, Matthew ^c   Fleet, David J ^a   Kohli, Pushmeet ^c  

^a UNIVERSITY OF TORONTO   (Canada)

^b University of Wisconsin   (United States)

^c MICROSOFT RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER VISION; DATA MINING; INFORMATION SCIENCE; LEARNING SYSTEMS; OPTIMIZATION; STOCHASTIC SYSTEMS;

DECISION TREE INDUCTION; GREEDY OPTIMIZATION; LINEAR COMBINATIONS; SPLITTING CRITERION; STANDARD ALGORITHMS; STOCHASTIC GRADIENT DESCENT; STRUCTURED PREDICTION; SUB-OPTIMAL TREES;

DECISION TREES;

EID: 84965166157     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. K. P. Bennett. Global tree optimization: A non-greedy decision tree algorithm. Computing Science and Statistics, pages 156-156, 1994.
2. K. P. Bennett and J. A. Blue. A support vector machine approach to decision trees. In Department of Mathematical Sciences Math Report No. 97-100, Rensselaer Polytechnic Institute, pages 2396-2401, 1997.
3. 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.
4. L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
5. L. Breiman, J. Friedman, R. A. Olshen, and C. J. Stone. Classification and regression trees. Chapman & Hall/CRC, 1984.
6. C. C. Chang and C. J. Lin. LIBSVM: a library for support vector machines, 2001.
7. A. Criminisi and J. Shotton. Decision Forests for Computer Vision and Medical Image Analysis. Springer, 2013.
8. Jerome H Friedman. Greedy function approximation: a gradient boosting machine. Annals of Statistics, pages 1189-1232, 2001.
9. J. Gall, A. Yao, N. Razavi, L. Van Gool, and V. Lempitsky. Hough forests for object detection, tracking, and action recognition. IEEE Trans. PAMI, 33(11):2188-2202, 2011.
10. T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning (Ed. 2). Springer, 2009.
11. L. Hyafil and R. L. Rivest. Constructing optimal binary decision trees is NP-complete. Information Processing Letters, 5(1):15-17, 1976.
12. J. Jancsary, S. Nowozin, and C. Rother. Loss-specific training of non-parametric image restoration models: A new state of the art. ECCV, 2012.
13. M. I. Jordan and R. A. Jacobs. Hierarchical mixtures of experts and the em algorithm. Neural Comput., 6(2):181-214, 1994.
14. E. Konukoglu, B. Glocker, D. Zikic, and A. Criminisi. Neighbourhood approximation forests. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012, pages 75-82. Springer, 2012.
15. B. Lakshminarayanan, D. M. Roy, and Y. H. Teh. Mondrian forests: Efficient online random forests. In Advances in Neural Information Processing Systems, pages 3140-3148, 2014.
16. J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine Learning, 4(2):227-243, 1989.
17. S. K. Murthy and S. L. Salzberg. On growing better decision trees from data. PhD thesis, John Hopkins University, 1995.
18. M. Norouzi, M. D. Collins, D. J. Fleet, and P. Kohli. Co2 forest: Improved random forest by continuous optimization of oblique splits. arXiv:1506.06155, 2015.
19. M. Norouzi and D. J. Fleet. Minimal Loss Hashing for Compact Binary Codes. ICML, 2011.
20. S. Nowozin. Improved information gain estimates for decision tree induction. ICML, 2012.
21. J. R. Quinlan. Induction of decision trees. Machine learning, 1(1):81-106, 1986.
22. J. Shotton, R. Girshick, A. Fitzgibbon, T. Sharp, M. Cook, M. Finocchio, R. Moore, P. Kohli, A. Criminisi, A. Kipman, et al. Efficient human pose estimation from single depth images. IEEE Trans. PAMI, 2013.
23. B. Taskar, C. Guestrin, and D. Koller. Max-margin Markov networks. NIPS, 2003.
24. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. ICML, 2004.
25. C. N. J. Yu and T. Joachims. Learning structural SVMs with latent variables. ICML, 2009.