Graph Learning

In the last few years we have been developing a series of models for classification and regression which are based on the geometrical structure of the data set. Particularly, we have concentrated our effors in the construction of a Gabriel Graph, since they have interesting properties that make its application to Machine Learning problems attractive. Large margin models for classification and for regression can be obtained without parameters provided by the user, so the models that we obtain are parameterless and appropriate for online and incremental learning. In addition integrated circuit implementation is feasible. 

- Students 

• Alex Damiany Assis (Ph.d) (PPGEE-UFMG)
• Murilo Vale Ferreira Menezes (M.Sc) (PPGEE-UFMG)
• Bruno César Cota Conceição ((Undergrad - UFOP))
• Gabriella Mendonça Santa Clara (Undergrad - UFOP)
• Wagner Bianchini Narde (Undergrad - UFOP)