A parameterless mixture model for large margin classification

Abstract

This paper presents a geometrical approach for obtaining large margin classifiers. The method aims at exploring the geometrical properties of the dataset from the structure of a Gabriel graph, which represents pattern relations according to a given distance metric, such as the Euclidean distance. Once the graph is generated, geometric vectors, analogous to SVM's support vectors are obtained in order to yield the final large margin solution from a mixture model approach. A preliminary experimental study with five real-world benchmarks showed that the method is promising.