Abstract
A form of prototypes defined as tuples of marginal probability distributions is introduced. Addition and subtraction operations on such prototypes are then described. A brief introduction to the mass assignment theory of the probability of fuzzy events is given, and it is shown how fuzzy sets can serve as conceptual descriptions of probability distributions. Hence fuzzy descriptions of prototypes can be derived and these can be used for inference as well as enabling rule based representations of a set of prototypes to be formed. A prototype induction algorithm, based on these ideas together with the addition and subtraction operations, is described. The potential of this approach is then illustrated by its application to a number of model and real world machine learning problems. ©1999 John Wiley & Sons, Inc.
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