Generalization of Elman Networks
نویسنده
چکیده
The Vapnik Chervonenkis dimension of Elman networks is innnite. Here, we nd constructions leading to lower bounds for the fat shattering dimension that are linear resp. of order log 2 in the input length even in the case of limited weights and inputs. Since niteness of this magnitude is equivalent to learnability, there is no a priori guarantee for the generalization capability of Elman networks.
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