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Future Directions for Machine Learning

A voting model semantics for fuzzy sets is assumed with the corresponding ideas of mass assignment theory. The use of fuzzy sets in this way will provide better interpolation, greater knowledge compression, less dependence on the effects of noisy data than if only crisp sets were used. We will see how easy and useful it is to use successful inference methods such as decision trees, probabilistic fuzzy logic type rules, Bayesian nets with attributes taking fuzzy values rather than crisp values.