Categorization in the pseudo-inverse neural network
Camilo Rodrigues Neto, José F. Fontanari · Journal of Physics A Mathematical and General · 1998
We investigate analytically the emergence of the categorization ability in the pseudo-inverse attractor neural network. More pointedly, we consider the problem of learning an extensive number of concepts by storing a finite number of examples s of each concept. We find that there is a critical value beyond which the categorization error, as measured by the average fraction of unstable sites in the concepts, decreases monotonically with s .