Work on the aforementioned problem led to the consideration of an iterative-like learning model, in which each of a learner’s conjectures may, in addition, depend upon the number of elements so far presented to the learner. Previous results had shown, for example, that U-shapes are unnecessary for explanatory learning, but are necessary for behaviorally correct learning. Iterative learning is a Gold-style learning model in which each of a learner’s output conjectures depends only upon the learner’s just previous conjecture and upon the most recent input element. A U-shape occurs when a learner first learns, then unlearns, and, finally, relearns, some target concept. This paper solves an important problem left open in the literature by showing that U-shapes are unnecessary in iterative learning.
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December 2022
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