Exceptions-based synthesis of Boolean functions as a core mechanism to perform concept learning
In this paper, an alternative approach to the synthesis of Boolean functions is presented. Such an approach can be useful in the field of concept learning as well, provided that the semantics of non-specified instances is changed accordingly (i.e., from a don't-care to an unknown semantics). The underlying framework relies on the concept of exception, an exception being, for example, a 0 grouped together with 1's while performing the synthesis. It is shown that an exceptions-based synthesis can be adopted as a core mechanism to perform concept learning in an n-dimensional Boolean space. A learning system is sketched where the decision of re-calculating classification rules can be arbitrarily delayed, as new examples, not consistent with the current hypothesis, can be integrated within the system by temporarily storing them as exceptions.
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