  Given two sets $\mathit{R}$ and $\mathit{B}$ of at most $\mathit{n}$ points
in the plane, we present efficient algorithms to find a two-line linear
classifier that best separates the "red" points in $\mathit{R}$ from the "blue"
points in $B$ and is robust to outliers. More precisely, we find a region
$\mathit{W}_\mathit{B}$ bounded by two lines, so either a halfplane, strip,
wedge, or double wedge, containing (most of) the blue points $\mathit{B}$, and
few red points. Our running times vary between optimal $O(n\log n)$ and
$O(n^4)$, depending on the type of region $\mathit{W}_\mathit{B}$ and whether
we wish to minimize only red outliers, only blue outliers, or both.
