  Missing responses is a missing data format in which outcomes are not always
observed. In this work we develop kernel machines that can handle missing
responses. First, we propose a kernel machine family that uses mainly the
complete cases. For the quadratic loss, we then propose a family of
doubly-robust kernel machines. The proposed kernel-machine estimators can be
applied to both regression and classification problems. We prove oracle
inequalities for the finite-sample differences between the kernel machine risk
and Bayes risk. We use these oracle inequalities to prove consistency and to
calculate convergence rates. We demonstrate the performance of the two proposed
kernel machine families using both a simulation study and a real-world data
analysis.
