Generalisation performance vs. architecture variations in constructive cascade networks

Abstract

Constructive cascade algorithms are powerful methods for training feedforward neural networks with automation of the task of specifying the size and topology of network to use. A series of empirical studies were performed to examine the effect of imposing constraints on constructive cascade neural network architectures. Building a priori knowledge of the task into the network gives better generalisation performance. We introduce our Local Feature Constructive Cascade (LoCC) and Symmetry Local Feature Constructive Cascade (SymLoCC) algorithms, and show them to have good generalisation and network construction properties on face recognition tasks. © 2009 Springer Berlin Heidelberg.