On the search for new learning rules for ANNs
In this paper, we present a framework where a learning rule can be optimized within a parametric learning rule space. We define what we callparametric learning rules and present a theoretical study of theirgeneralization properties when estimated from a set of learning tasks and tested over another set of tasks. We corroborate the results of this study with practical experiments.