Although general network learning rules are of undeniable interest, it is generally agreed that successful accounts of learning must incorporate domain-specific, a priori knowledge. Such knowledge might be used, for example, to determine the structure of a network or its initial weights. The author discusses a third possibility in which domain-specific knowledge is incorporated directly in a network learning rule via a set of constraints on activations. The approach uses the notion of a forward model to give constraints a domain-specific interpretation. This approach is demonstrated with several examples from the domain of motor learning.
CITATION STYLE
Jordan, M. I. (1989). Generic constraints on underspecified target trajectories (pp. 217–225). Publ by IEEE. https://doi.org/10.1109/ijcnn.1989.118584
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