Bidirectional convergence: A cognitive approach to generalisation

We present a cognitive approach to generalisation, Bidirectional Convergence. This is the implementation of a cognitive process we call concept crystallisation, whereby a concept is formed gradually from initially many possibilities which converge to a single possibility under the weight of a series of learning instances shown over a period of time. Bidirectional Convergence (BDC) is a form of concept crystallisation that represents the alternative possible concepts through. Boundary versions of the concept during learning. BDC is an abstraction of Mitchell´s symbolic concept learning technique (1982). We describe how BDC is evolved from Mitchell´s technique into a form suitable for incorporation into neural networks, BDC is shown to provide a best-fit to given problems