Towards Optimal Learning from Very Small Data Sets
نویسنده
چکیده
The problem of generalization in neural networks, that is, how well a network will perform on unseen data, is has received much attention recently. In this paper we present an approach to generating learning algorithms which have the potential to generalize from very small training sets. We believe that in this paper outlines a new, although potentially computationally expensive, approach to optimal learning in Neural Networks.
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تاریخ انتشار 1994