Universal approximation theorem

Known as: Cybenko Theorem, Universal approximator 
In the mathematical theory of artificial neural networks, the universal approximation theorem states that a feed-forward network with a single hidden… (More)
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Papers overview

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Review
2016
Review
2016
Next-generation sequencing (NGS) is a rapidly evolving set of technologies that can be used to determine the sequence of an… (More)
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Highly Cited
2010
Highly Cited
2010
Deep belief networks (DBN) are generative models with many layers of hidden causal variables, recently introduced by Hinton… (More)
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2006
2006
The paper deals with an extension theorem by Costakis and Vlachou on simultaneous approximation for holomorphic function to the… (More)
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2005
2005
Cascade correlation (CC) constitutes a training method for neural networks that determines the weights as well as the neural… (More)
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2003
2003
Most existing universal approximation results for fuzzy systems are based on the assumption that we use t conorms and t conorms… (More)
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Highly Cited
2003
Highly Cited
2003
We investigate the approximation ability of a multi layer perceptron (MLP) network when it is extended to the complex domain. The… (More)
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Highly Cited
1998
Highly Cited
1998
In this paper, we propose the approximate transformable technique, which includes the direct transformation and indirect… (More)
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Highly Cited
1995
Highly Cited
1995
A theoretical justification for the random vector version of the functional-link (RVFL) net is presented in this paper, based on… (More)
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Highly Cited
1990
Highly Cited
1990
A neural network with a single layer of hidden units of gaussian type is proved to be a universal approximator for real-valued… (More)
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Highly Cited
1989
Highly Cited
1989
K.M. Hornik, M. Stinchcombe, and H. White (Univ. of California at San Diego, Dept. of Economics Discussion Paper, June 1988; to… (More)
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