Abstract

In this article we propose a generic framework for Meta Learning (MTL) of parameters and models used in the optimization of inference systems. This generic optimization approach can be applied to any hypothesis or learner as long as the optimizer can support the parameters and model values required. This proposal is evaluated classifying the position and type of users in a social database. The obtained results are contrasted with other classification methods and with manually optimized inference systems.