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Evolution of Cubic Spline Activation Functions for Artificial Neural Networks (2001)  (Make Corrections)  
Helmut A. Mayer, Roland Schwaiger
Proceedings of the 10th Portuguese Conference on Artificial Intelligence (EPIA 2001), LNAI 2258



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Abstract: The most common (or even only) choice of activation functions (AFs) for multi{layer perceptrons (MLPs) widely used in research, engineering and business is the logistic function. Among the reasons for this popularity are its boundedness in the unit interval, the function's and its derivative's fast computability, and a number of amenable mathematical properties in the realm of approximation theory. However, considering the huge variety of problem domains MLPs are applied in, it is intriguing to ... (Update)

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BibTeX entry:   (Update)

@INPROCEEDINGS(Mayer01d,
 AUTHOR = "Helmut A. Mayer and Roland Schwaiger",
 TITLE = "{Evolution of Cubic Spline Activation Functions for Artificial 
Neural Networks}",
 BOOKTITLE = "Proceedings of the 10th Portuguese Conference on 
Artificial Intelligence (EPIA 2001), LNAI 2258",
 YEAR = 2001,
 PAGES = "63--73",
 PUBLISHER = "Springer",
 MONTH = "December")
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221   Perceptrons: Introduction to Computational Geometry (context) - Minsky, Papert - 1988
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31   SNNS Stuttgart Neural Network Simulator - Zell, Mamier et al. - 1994
15   Learning and approximation capabilities of adaptive spline a.. - Vecci, Piazza et al. - 1998
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3   Neural networks with periodic and monotonic activation funct.. (context) - Sopena, Romero et al. - 1999
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