Talks and Poster Presentations (with Proceedings-Entry):
G. Pölzlbauer, T. Lidy, A. Rauber:
"Decision Manifolds: Classification Inspired by Self-Organization";
Talk: International Workshop on Self-Organizing Maps (WSOM'07),
- 2007-09-06; in: "6th International Workshop on Self-Organizing Maps",
H. Ritter, R. Haschke (ed.);
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.
Decision Manifolds, supervised learning, ensemble classification
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.