A support vector approach to censored targets

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2007, Pages 655-660

  Shivaswamy, Pannagadatta K ^a   Chu, Wei ^a   Jansche, Martin ^b  

^a Columbia University ^*  (United States)

^b GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX PROGRAMMING; DATA-SETS; GENERALIZATION CAPACITY; INTERNATIONAL CONFERENCES; OPTIMAL SOLUTIONS; PERFORMANCE IMPROVEMENTS; REAL LINE; SURVIVAL ANALYSIS; SURVIVAL TIME; TRAINING DATA; TRAINING SPEED;

ADMINISTRATIVE DATA PROCESSING; DATA MINING; DECISION SUPPORT SYSTEMS; FOOD PROCESSING; INFORMATION MANAGEMENT; MINING; SEARCH ENGINES; TARGETS; VECTORS;

BIOINFORMATICS;

EID: 49749098490     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2007.93     Document Type: Conference Paper

References (17)

1. G. C. Cawley, N. L. C. Talbot, G. J. Janacek, and M. W. Peck. Sparse Bayesian kernel survival analysis for modeling the growth domain of microbial pathogens. IEEE Transactions of Neural Networks, 17(2):471-481, 2006.
2. W. Chu and Z. Ghahramani. Preference learning with gaussian processes. In ICML '05: Proceedings of the 22nd international conference on Machine learning, pages 137-144, 2005.
3. W. Chu and S. S. Keerthi. Support vector ordinal regression. Neural Computation, 19:792-815, 2007.
4. A. E. Gehan. A generalized Wilcoxon test for comparing arbitrarily singly-censored samples. Biometrika, 52:203-223, 1965.
5. S. Har-Peled, D. Roth, and D. Zimak. Constraint classification for multiclass classification and ranking. In NIPS, pages 785-792. MIT Press, 2003.
6. F. E. Harrell Jr. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer Series in Statistics. Springer, 2001.
7. T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 169-185. MIT Press, 1998.
8. T. Joachims. Training linear svms in linear time. In KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 217-226, 2006.
9. V. Kecman. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. MIT Press, Cambridge, MA, USA, 2001.
10. S. S. Keerthi and D. DeCoste. A modified finite newton method for fast solution of large scale linear svms. Journal of Machine Learning Research, 6:341-361, Mar 2005.
11. S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy. Improvements to SMO algorithm for SVM regression. IEEE Transactions on Neural Networks, 11(5):1188-1193, 2000.
12. D. Lee. An improved cluster labeling method for support vector clustering. IEEE Trans. Pattern Anal. Mach. Intell., 27(3):461-464, 2005. Member-Jaewook Lee.
13. H. B. Mann and D. R. Whitney. On a test of whether one of 2 random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18:50-60, 1947.
14. J. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schlkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 185-208. MIT Press, Cambridge, MA, USA, 1999.
15. P. K. Shivaswamy, W. Chu, and M. Jansche. A support vector approach to censored targets. Technical Report CCLS-07-03, CCLS, Columbia University, 2007.
16. V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.
17. M. H. Zweig and G. Campbell. Receiver-operating characteristic (roc) plots: a fundamental evaluation tool in clinical medicine. Clinical chemistry, 39(4):561-577, 1993.