Generalization in Sigma-Pi Networks

Abstract

In this paper, we investigate generalization in supervised feedforward Sigma-pi nets with particular reference to means of augmentation of generalization of the network for specific tasks. The work was initiated because logical (digital) neural networks of this type do not function in the same manner as the more normal semi-linear unit, hence the general principle behind Sigma-pi networks generalization required examination, to enable one to put forward means of augmenting their generalization abilities. The paper studies four methods, two of which are novel methodologies for enhancing Sigma-pi networks generalization abilities. The networks are hardware realizable and the Sigma-pi units are logical (digital) nodes that respond to their input patterns in addressable locations, the locations (site-values) then define the probability of the output being a logical ‘1’. In this paper, we evaluate the performance of Sigma-pi nets with perceptual problems (in pattern recognition). This was carried out by compar...