Cost-sensitive tree of classifiers

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 1, 2013, Pages 133-141

  Xu, Zhixiang ^a   Kusner, Matt J ^a   Weinberger, Kilian Q ^a   Chen, Minmin ^a  

^a WASHINGTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE EXTRACTION; FORESTRY; INDUSTRIAL APPLICATIONS; LEARNING ALGORITHMS; LEARNING SYSTEMS; SEARCH ENGINES;

COMPUTATIONAL COSTS; COST-SENSITIVE; E-MAIL SPAM; EXTRACTION TIME; INDIVIDUAL PATHS; INPUT SPACE; SUBPARTITION; TEST INPUTS;

COSTS;

ALGORITHMS; COSTS; FORESTRY;

EID: 84897496647     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Bengio, S., Weston, J., and Grangier, D. Label embedding trees for large multi-class tasks. NIPS, 23:163-171, 2010.
2. Boyd, S.P. and Vandenberghe, L. Convex optimization. Cambridge Univ Pr, 2004.
3. Breiman, L. Classification and regression trees. Chapman & Hall/CRC, 1984.
4. Busa-Fekete, R., Benbouzid, D., Kégl, B., et al. Fast classification using sparse decision dags. In ICML, 2012.
5. Cambazoglu, B.B., Zaragoza, H., Chapelle, O., Chen, J., Liao, C., Zheng, Z., and Degenhardt, J. Early exit optimizations for additive machine learned ranking systems. In WSDM'3, pp. 411-420, 2010.
6. Chapelle, O. and Chang, Y. Yahoo! learning to rank challenge overview. In JMLR: Workshop and Conference Proceedings, volume 14, pp. 1-24, 2011.
7. Chapelle, O., Shivaswamy, P., Vadrevu, S., Weinberger, K., Zhang, Y., and Tseng, B. Boosted multi-task learning. Machine learning, 85(1):149-173, 2011.
8. Chen, M., Xu, Z., Weinberger, K. Q., and Chapelle, O. Classifier cascade for minimizing feature evaluation cost. In AISTATS, 2012.
9. Deng, J., Satheesh, S., Berg, A.C., and Fei-Fei, L. Fast and balanced: Efficient label tree learning for large scale object recognition. In NIPS, 2011.
10. Dredze, M., Gevaryahu, R., and Elias-Bachrach, A. Learning fast classifiers for image spam. In proceedings of the Conference on Email and Anti-Spam (CEAS), 2007.
11. Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. Least angle regression. The Annals of Statistics, 32(2): 407-499, 2004. (Pubitemid 41250302)
12. Fleck, M., Forsyth, D., and Bregler, C. Finding naked people. ECCV, pp. 593-602, 1996.
13. Friedman, J.H. Greedy function approximation: a gradient boosting machine. The Annals of Statistics, pp. 1189-1232, 2001. (Pubitemid 33405972)
14. Gao, T. and Koller, D. Active classification based on value of classifier. In NIPS, pp. 1062-1070. 2011.
15. Grubb, A. and Bagnell, J. A. Speedboost: Anytime prediction with uniform near-optimality. In AISTATS, 2012.
16. Järvelin, K. and Kekalainen, J. Cumulated gain-based evaluation of IR techniques. ACM TOIS, 20(4):422-446, 2002. (Pubitemid 44642296)
17. Jordan, M.I. and Jacobs, R.A. Hierarchical mixtures of experts and the em algorithm. Neural computation, 6 (2):181-214, 1994.
18. Karayev, S., Baumgartner, T., Fritz, M., and Darrell, T. Timely object recognition. In Advances in Neural Information Processing Systems 25, pp. 899-907, 2012.
19. Kowalski, M. Sparse regression using mixed norms. Applied and Computational Harmonic Analysis, 27(3):303-324, 2009.
20. Lefakis, L. and Fleuret, F. Joint cascade optimization using a product of boosted classifiers. In NIPS, pp. 1315-1323. 2010.
21. Pujara, J., Daumé III, H., and Getoor, L. Using classifier cascades for scalable e-mail classification. In CEAS, 2011.
22. Saberian, M. and Vasconcelos, N. Boosting classifier cascades. In Lafferty, J., Williams, C. K. I., Shawe-Taylor, J., Zemel, R.S., and Culotta, A. (eds.), NIPS, pp. 2047-2055. 2010.
23. Schölkopf, B. and Smola, A.J. Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT press, 2001.
24. Viola, P. and Jones, M.J. Robust real-time face detection. IJCV, 57(2):137-154, 2004.
25. Wang, J. and Saligrama, V. Local supervised learning through space partitioning. In Advances in Neural Information Processing Systems 25, pp. 91-99, 2012.
26. Weinberger, K.Q., Dasgupta, A., Langford, J., Smola, A., and Attenberg, J. Feature hashing for large scale multitask learning. In ICML, pp. 1113-1120, 2009.
27. Xu, Z., Weinberger, K., and Chapelle, O. The greedy miser: Learning under test-time budgets. In ICML, pp. 1175-1182, 2012.
28. Zheng, Z., Zha, H., Zhang, T., Chapelle, O., Chen, K., and Sun, G. A general boosting method and its application to learning ranking functions for web search. In NIPS, pp. 1697-1704. Cambridge, MA, 2008.