Two Constructive Methods for Designing Compact Feedforward Networks of Threshold Units

Abstract

We propose two algorithms for constructing and training compact feedforward networks of linear threshold units. The SHIFT procedure constructs networks with a single hidden layer while the PTI constructs multilayered networks. The resulting networks are guaranteed to perform any given task with binary or real-valued inputs. The various experimental results reported for tasks with binary and real-valued inputs indicate that our methods compare favorably with alternative procedures deriving from similar strategies, both in terms of size of the resulting networks and of their generalization properties.