An Experimental Comparison of Symbolic and Connectionist Learning Algorithms (1989)
Raymond J. Mooney, J.W. Shavlik, G. Towell and A. Gove
Despite the fact that many symbolic and connectionist (neural net) learning algorithms are addressing the same problem of learning from classified examples, very little is known regarding their comparative strengths and weaknesses. This paper presents the results of experiments comparing the ID3 symbolic learning algorithm with the perceptron and back-propagation connectionist learning algorithms on several large real-world data sets. The results show that ID3 and perceptron run significantly faster than does back-propagation, both during learning and during classification of novel examples. However, the probability of correctly classifying new examples is about the same for the three systems. On noisy data sets there is some indication that back-propagation classifies more accurately.
In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pp. 775-780, Detroit, MI, August 1989. Reprinted in ``Readings in Machine Learning'', Jude W. Shavlik and T. G. Dietterich (eds.), Morgan Kaufman, San Mateo, CA, 1990..

Raymond J. Mooney Faculty mooney [at] cs utexas edu