  We study the problem of binary classification from the point of view of
learning convex polyhedra in Hilbert spaces, to which one can reduce any binary
classification problem. The problem of learning convex polyhedra in
finite-dimensional spaces is sufficiently well studied in the literature. We
generalize this problem to that in a Hilbert space and propose an algorithm for
learning a polyhedron which correctly classifies at least $1- \varepsilon$ of
the distribution, with a probability of at least $1 - \delta,$ where
$\varepsilon$ and $\delta$ are given parameters. Also, as a corollary, we
improve some previous bounds for polyhedral classification in
finite-dimensional spaces.
