Learning in knowledge based systems, a possibilistic approach
Let us now make the final conclusions concerning the learning process by examples based on the concepts of a possibility distribution function and a rough set. As was shown the interpretation JS has the power of reducing the uncertainty whether an element belongs or does not belong to a concept to be learned. This is an improvement over the interpretation MS which can not provide any information for elements in the boundary. It can be observed that the computed function GS gets closer to the interpretation JS as the number of the learned concepts grows larger. The measure of learning defined by us reflects the effect of arranging the set of concepts to be learned into a particular sequence. The optimization method that we have outlined can be used to improve the learning process.
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