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Decision Manifolds: Classification Inspired by Self-Organization
Pölzlbauer, Georg ; Lidy, Thomas ; Rauber, Andreas
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 the classifiction results of 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
||Faculty of Technology, Research Groups in Informatics
||Data processing, computer science, computer systems
Pölzlbauer, Georg ; Lidy, Thomas ; Rauber, Andreas (2007) Decision Manifolds: Classification Inspired by Self-Organization.