Name
Abstract | ||
|---|---|---|
We present a type of single-hidden layer feedforward neural networks with the Gaussian activation function. First, we give a new and quantitative proof of the fact that a single layer neural networks with n + 1 hidden neurons can learn n + 1 distinct samples with zero error. Then we give approximate interpolants. They can approximate interpolate, with arbitrary precision, any set of distinct data in one or several dimensions. They can uniformly approximate any continuous function of one variable. © 2007 IEEE. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICNC.2007.498 | ICNC |
Keywords | Field | DocType |
activation function,feedforward neural network,function approximation,neural network,gaussian processes | Universal approximation theorem,Feedforward neural network,Mathematical optimization,Rectifier (neural networks),Activation function,Recurrent neural network,Types of artificial neural networks,Artificial intelligence,Gaussian process,Artificial neural network,Mathematics,Machine learning | Conference |
Volume | Issue | ISSN |
1 | null | null |
ISBN | Citations | PageRank |
0-7695-2875-9 | 3 | 0.41 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuli Han | 1 | 159 | 22.91 |
| Muzhou Hou | 2 | 50 | 4.49 |