In this paper, we present a neural classifier algorithm
that locally approximates the decision surface of labeled data by a
patchwork of separating hyperplanes, which are arranged under
certain topological constraints similar to those of self-organizing
maps (SOMs).We take advantage of the fact that these boundaries
can often be represented by linear ones connected by a low-dimensional
nonlinear manifold, thus influencing the placement of
the separators. The resulting classifier allows for a voting scheme
that averages over the classification results of neighboring hyperplanes.
Our algorithm is computationally efficient both in terms
of training and classification. Further, we present a model selection
method to estimate the topology of the classification boundary.
We demonstrate the algorithm’s usefulness on several artificial and
real-world data sets and compare it to the state-of-the-art supervised