Third-order generalization: A new approach to categorizing higher-order generalization

Generalization, in its most basic form, is an artificial neural network's (ANN's) ability to automatically classify data that were not seen during training. This paper presents a framework in which generalization in ANNs is quantified and different types of generalization are viewed as orders. The ordering of generalization is a means of categorizing different behaviours. These orders enable generalization to be evaluated in a detailed and systematic way. The approach used is based on existing definitions which are augmented in this paper. The generalization framework is a hierarchy of categories which directly aligns an ANN's ability to perform table look-up, interpolation, extrapolation, and hyper-extrapolation tasks. The framework is empirically validated. Validation is undertaken with three different types of regression task: (1) a one-to-one (o

• Type: Journal
• Year: 2008
• Where: IJON
• Authors: Richard Neville
• Keywords: Artificial Neural Network, Generalization Framework, IJON 2008, Neural