We present a classifier algorithm that
approximates the decision surface of labeled data by a
patchwork of separating hyperplanes. The hyperplanes are
arranged in a way inspired by how Self-Organizing Maps
are trained. We take advantage of the fact that the boundaries
can often be approximated by linear ones connected
by a low-dimensional nonlinear manifold. The resulting
classifier allows for a voting scheme that averages over
neighboring hyperplanes. Our algorithm is computationally
efficient both in terms of training and classification.
Further, we present a model selection framework for estimation
of the paratmeters of the classification boundary,
and show results for artificial and real-world data sets.