Gabriel Graph for Dataset Structure and Large Margin Classification: A Bayesian Approach
Luiz C. B. Torres, Cristiano Leite de Castro, Antônio P. Braga · The European Symposium on Artificial Neural Networks · 2015
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 vec- tors, analogous to SVM's support vectors are obtained in order to yield the final large margin solution from a Gaussian mixture model approach. Preliminary experiments have shown that the solutions obtained with the proposed method are close to those obtained with SVMs.