This report presents and proposes several methods to improve the capacity of generalization of the learning algorithms in a context of financial decision-making. These methods, overall, aim at controlling the capacity of the learning algorithms in order to limit the problem of the over-training, which is one of most pernicious in finance because of the high levels of noise met in practice. We propose some tracks of research in order to improve the algorithms and results already obtained.