Incremental Learning From Symbolic Objects
This paper deals with discrimination on real-world data when examples as well as partial rules are initially available. As the learning base is both noisy and insufficiently representative, our approach is oriented towards successive approximations of a discriminant rule base; the aim is to predict conclusions according to further examples.
A sub-optimal generalization leads to an approximative rule base including redundancy (strongly overlapping, rules) and errors.
A change of representation called reduction transforms the refinement of the previous rule base into a new learning problem. Iteration of the generalization then defines a second rule set, refining and correcting the previous one, and so on.
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