Support Vector Regression for Right Censored Data
Machine Learning
2013-01-15 v2 Statistics Theory
Statistics Theory
Abstract
We develop a unified approach for classification and regression support vector machines for data subject to right censoring. We provide finite sample bounds on the generalization error of the algorithm, prove risk consistency for a wide class of probability measures, and study the associated learning rates. We apply the general methodology to estimation of the (truncated) mean, median, quantiles, and for classification problems. We present a simulation study that demonstrates the performance of the proposed approach.
Cite
@article{arxiv.1202.5130,
title = {Support Vector Regression for Right Censored Data},
author = {Yair Goldberg and Michael R. Kosorok},
journal= {arXiv preprint arXiv:1202.5130},
year = {2013}
}
Comments
In this version, we strengthened the theoretical results and corrected a few mistakes