Inductive inference and neural nets

Abstract

We present an interesting experiment comparing human learning (quick modelling) to symbolic learning (ID3), neural net learning (perceptron), and pattern learning (Pao-Hu). From this experiment we find that humans, neural nets, and the Pao-Hu procedure favour a decision tree different from that preferred by the ID3 machine learning algorithm. Implications for the ‘strong convergence hypothesis’ between neural network and machine learning are discussed, with emphasis here on a ‘stronger convergence hypothesis’ between neural networks and humans. We further compare the generalization properties of the four approaches, and we introduce a method of ‘backprediction’ as a measure of the relative quality of generalizations. The relationships of our findings to other recent investigations comparing symbolic and neural learning algorithms are discussed.