A parameterless mixture model for large margin classification

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.