Keywords

training set, generalization

Abstract

There are many ways for a learning system to generalize from training set data. There is likely no one style of generalization which will solve all problems better than any other style, for different styles will work better on some applications than others. This paper presents several styles of generalization and uses them to suggest that a collection of such styles can provide more accurate generalization than any one style by itself. Empirical results of generalizing on several real-world applications are given, and comparisons are made on the generalization accuracy of each style of generalization. The empirical results support the hypothesis that using multiple generalization styles can improve generalization accuracy.

Original Publication Citation

Wilson, D. R. and Martinez, T. R., "The Importance of Multiple Styles of Generalization", Proceedings of the International Conference on Artificial Neural Networks and Expert Systems ANNES'93, pp. 54-57, 1993.