Meta-learning and neurocomputing – a new perspective for computational intelligence

In this chapter an analysis of computational mechanisms of induction is brought forward, in order to assess the potentiality of meta-learning methods versus the common base-learning practices. To this aim, firstly a formal investigation of inductive mechanisms is accomplished, sketching a distinction between fixed and dynamical bias learning. Then a survey is presented with suggestions and examples which have been proposed in literature to increase the efficiency of common learning algorithms. The peculiar laboratory for this kind of investigation is represented by the field of connectionist learning. To explore the meta-learning possibilities of neural network systems, knowledge-based neurocomputing techniques are considered. Among them, some kind of hybridisation strategies are particularly analysed and addressed as peculiar illustrations of a new perspective of Computational Intelligence.