  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.
