Abstract
The first part of this article surveys different current trends in mathematical learning theory. The main divisions of the subject covered are stimulus-response theory, language learning, formal learning theory, perceptrons, cellular automata, and neural networks. The second part is concerned with extending the ideas of stimulus-response theory to universal computation. This is done by using register machines rather than Turing machines. The main theorem is that any partial recursive function can be asymptotically learned by a register learning model. In the discussion of this result the emphasis is on the need for a carefully organized hierarchy of concepts in order to have a rate of learning that is realistic for either organisms or machines.
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Suppes, P. (1989). Current Directions in Mathematical Learning Theory. In: Roskam, E.E. (eds) Mathematical Psychology in Progress. Recent Research in Psychology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83943-6_1
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DOI: https://doi.org/10.1007/978-3-642-83943-6_1
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