Boosting with structural sparsity

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 297-304

  Duchi, John ^a   Singer, Yoram ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADABOOST; COORDINATE DESCENT; EMPIRICAL RESULTS; GRADIENT BASED; IN-BUILDINGS; MIXED-NORM; MULTICLASS PREDICTION; PARAMETER SPACES; STOPPING CRITERIA; USEFUL PROPERTIES;

ADAPTIVE BOOSTING; COMMUNICATION CHANNELS (INFORMATION THEORY); ECHO SUPPRESSION;

ROBOT LEARNING;

EID: 71149103193     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Bertsekas, D. (1999). Nonlinear programming. Athena Scientific.
2. Collins, M., Schapire, R., & Singer, Y. (2002). Logistic regression, AdaBoost and Bregman distances. Machine Learning, 47, 253-285.
3. Dekel, O., Shalev-Shwartz, S., & Singer, Y. (2005). Smooth epsilon-insensitive regression by loss symmetrization. J. Machine Learning Research, 6, 711-741.
4. Dudík, M., Phillips, S. J., & Schapire, R. E. (2007). Maximum entropy density estimation with generalized regularization and an application to species distribution modeling. J. Machine Learning Research, 8, 1217-1260.
5. Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28, 337-374.
6. Friedman, J., Hastie, T., & Tibshirani, R. (2007). Pathwise coordinate optimization. Annals of Applied Statistics, 1, 302-332.
7. 1-regularized logistic regression. J. Machine Learning Research, 8, 1519-1555.
8. Lewis, D., Yang, Y., Rose, T., & Li, F (2004). RCV1: A new benchmark collection for text categorization research. J. Machine Learning Research, 5, 361-397.
9. Meir, R., & Rätsch, G. (2003). An introduction to boosting and leveraging. Advanced Lectures on Machine Learning (pp. 119-184). Springer.
10. Negahban, S., & Wainwright, M. (2008). Phase transitions for high-dimensional joint support recovery. Advances in Neural Information Processing Systems 22.
11. Obozinski, G., Taskar, B., & Jordan, M. (2007). Joint covariate selection for grouped classification (Technical Report 743). Dept. of Statistics, University of California Berkeley.
12. Spiegelhalter, D., & Taylor, C. (1994). Machine learning, neural and statistical classification. Ellis Horwood.
13. Warmuth, M., Liao, J., & Ratsch, G., (2006). Totally corrective boosting algorithms that maximize the margin. Proceedings of the 23rd International Conference on Machine Learning (pp. 1001-1008).
14. Zhang, T. (2008). Adaptive forward-backward greedy algorithm for sparse learning with linear models. Advances in Neural Information Processing Systems 22.
15. Zhang, T., & Yu, B. (2005). Boosting with early stopping: Convergence and consistency. Annals of Statistics, 33, 1538-1579.
16. Zhao, P., & Yu, B. (2006). On model selection consistency of Lasso. J. Machine Learning Research, 7, 2541-2567.