Prediction of time to event for censored data: Ridge regression with linear constraints in kernel space

Proceedings of the International Joint Conference on Neural Networks

Volumn 2, Issue , 2005, Pages 1033-1038

  Bagotskaya, Natasha ^a   Lossev, Ilia ^a   Losseva, Ninel ^a   Parakhin, Mikhail ^a  

^a Parascript LLC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CENSORED DATA; KERNEL FUNCTION; RIDGE REGRESSION;

CONSTRAINT THEORY; DISEASES; FUNCTIONS; REGRESSION ANALYSIS; TUMORS;

DATA REDUCTION;

EID: 33745960750     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2005.1555995     Document Type: Conference Paper

References (17)

1. J. P. Klein and M. L. Moeschberger. Survival analysis. Techniques for Censored and Truncated data. Springer, 2003.
2. W. Stute, "Consistent estimation under random censorship when oovariables are present", J. of Multvariate analyses, vol. 45, pp. 89-103, 1993.
3. M. Kohler, K. Mathe, M. Pinter. "Prediction fiorn randomly right censored data " Journal of Multivariate Analysis, vol. 80, pp. 73-100, 2002.
4. V. N. Vapnik. Statistical learning theory. New York: Wiley, 1998.
5. M. E Tipping. "The Relevance Vector Machine ". Advances in Neural Information Processing Systems 12. MIT Press, 2000.
6. D. Faraggi, R. Simon, "A Neural Network Mood for Survival Data" Statistic in Medicine, vol. 14, pp. 73-82, 1995.
7. R.M. Ripley:." Survrnet: Neural network model for survival models". 1998. Http://www.stas.ox.ac.uk/pub/swin/survnetzip
8. D. J.Sargent. "Comparison of Artificial Neural Networks with Other Statistical Approaches"., Cancer, vol. 91, No 8.2001.
9. L. Onho-Maghado."A Comparison of Cox Proportional Hazards and Artificial Neural Network Models for Medical Prognosis." Comput. Biol. Med. Vol.. 27, No. 1, pp. 55-66, 1997
10. L.J. van't Veer, H. Dai, M.J. van de Vijver., et al. "A Gene-Expression Signature as a Predictor of Survival in Breast Cancer." Nature; vol. 415, pp. 530-536.2002
11. L. J. van't Veer, H. Dai, M.J. van de Vijver., et al.," A Gene-Expression Signature as a Predictor of Survival in Breast Cancer". NEJM, vol. 347, pp. 1999-2009, 2002,
12. C. M. Bishop, Neural Networks for Pattern Recognition., Oxford University Press, 1995.
13. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning Springer, 2001.
14. J. Nocedal, S. J. Wright. Numerical Optimcation. Springer, 1999
15. N. E. Ayat, M. Cheriet, C. Y. Suen," Kmod-a New Support Vector Machine Kernel for Pattern Recognition. Application to Digit Image Recognition."ICDAR, pp. 1215-1219, 2001.
16. Chih-Chung Chang, Chin-Jen Lin, "LIBSVM: a Library for Support Vector Machine." http:/www.csie.ntu.edu.tw/-cjilin/papers/libsvm.pdf, 2004.
17. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Wapnik. "Feature Selections for SVM" Advances in Information Processing Systems 13, MIT Press, 2001.