An Integral Upper Bound for Neural Network Approximation
نویسندگان
چکیده
Complexity of one-hidden-layer networks is studied using tools from nonlinear approximation and integration theory. For functions with suitable integral representations in the form of networks with infinitely many hidden units, upper bounds are derived on the speed of decrease of approximation error as the number of network units increases. These bounds are obtained for various norms using the framework of Bochner integration. Results are applied to perceptron networks.
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ورودعنوان ژورنال:
- Neural computation
دوره 21 10 شماره
صفحات -
تاریخ انتشار 2009