Furthermore it has provided theoretical certitude on the effectivness of the procedure. However, the iterative optimisation method associated with the diversity and flexibility of the architecture of a firing graph opens doors to further applications, notably in the field of inverse problem and in the very hype field of machine learning. Indeed in supervised classification, we are given a dataset composed of features that may be numerical or categorical description of samples and targets that specify the class of samples. If we assume that the activation of a target is a combination of latent factors's activations and that we operate the minimum transformation of features so that they take the form of a measure grid, a light layer of procedures could turn our solution into a supervised classificator. The specificity of such a learner would give it an interesting position in the supervised learning landscape. Indeed, its iterative optimisation and flexible architecture could make it an adaptative learner, that scale to large dataset, with minimum processing work on raw data, in the manner of a neural network. Yet unlike neural network the algorithm handle very efficiently categorical or sparse feature space. Furthermore, compared to the most advanced tree based classification, its flexible architecture is more suitable to learning update and on-the-fly evaluation or addition of new features. Finally, given the hype granted to the field of machine learning nowaday, both in the scientific comunity and civil society, it would be common sense to orient this piece of research to this field.