Evolution of Cubic Spline Activation Functions for Artificial Neural Networks
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Publisher
Springer Berlin Heidelberg
Link
http://link.springer.com/content/pdf/10.1007/3-540-45329-6_10
Reference11 articles.
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3. Barron, A.R.: Universal approximation bounds for superpositions of a sigmoidal function. IEEE Transactions on Information Theory 39 (1993) 930–944
4. Liu, Y., Yao, X.: Evolutionary Design of Artificial Neural Networks with Different Nodes. In: Proceedings of the Third IEEE International Conference on Evolutionary Computation. (1996) 570–675
5. Vecci, L., Piazza, F., Uncini, A.: Learning and approximation capabilities of adaptive spline activation function neural networks. Neural Networks (1998) 259–270
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