Support vector regression for censored data

Details Support vector regression for censored data Support vector regression for censored data

2011-04-26

2011-04-26

Gaffin, Jeffrey A (Department: 2129)

Data processing: artificial intelligence

Adaptive system

Reexamination Certificate

active

07933848

ABSTRACT:
A method of producing a model for use in predicting time to an event includes obtaining multi-dimensional, non-linear vectors of information indicative of status of multiple test subjects, at least one of the vectors being right-censored, lacking an indication of a time of occurrence of the event with respect to the corresponding test subject, and performing regression using the vectors of information to produce a kernel-based model to provide an output value related to a prediction of time to the event based upon at least some of the information contained in the vectors of information, where for each vector comprising right-censored data, a censored-data penalty function is used to affect the regression, the censored-data penalty function being different than a non-censored-data penalty function used for each vector comprising non-censored data.

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Annette M. Molinaro, Sandrine Dudoit, and Mark J. van der Laan, “Tree-based Multivariate Regression and Density Estimation with Right-Censored Data” (Sep. 2003). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 135.
Alex J. Smola, Bernhard Scholkopf. “A Tutorial on Support Vector Regression” NeuroColt2 Technical Report Series Oct. 1998.
Alain Rakotomamonjy “Variable Selection Using SVM based Criteria” Journal of Machine Learning Research Mar. 2003.

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