Content deleted Content added
Leitisvatn (talk | contribs) m Modify the label of the graph, the parameters accompanying the input x should not be called weights, otherwise it is easy to cause misunderstandings |
→Network architecture: citeseerx→cite web | Alter: url. URLs might have been anonymized. | Use this tool. Report bugs. | #UCB_Gadget |
||
Line 32:
:<math>\varphi(\mathbf{x}) = \sum_{i=1}^N a_i \rho(||\mathbf{x}-\mathbf{c}_i||)</math>
where <math>N</math> is the number of neurons in the hidden layer, <math>\mathbf c_i</math> is the center vector for neuron <math>i</math>, and <math>a_i</math> is the weight of neuron <math>i</math> in the linear output neuron. Functions that depend only on the distance from a center vector are radially symmetric about that vector, hence the name radial basis function. In the basic form, all inputs are connected to each hidden neuron. The [[Norm (mathematics)|norm]] is typically taken to be the [[Euclidean distance]] (although the [[Mahalanobis distance]] appears to perform better with pattern recognition<ref>{{cite
|last1=Beheim|first1=Larbi
|last2=Zitouni|first2=Adel
Line 38:
|date=January 2004
|title=New RBF neural network classifier with optimized hidden neurons number
|url=https://www.researchgate.net/publication/254467552
}}</ref><ref>{{cite conference
|conference=Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society
Line 50:
|publication-date=6 January 2003
|volume=3
|pages=
|doi=10.1109/IEMBS.2002.1053230
|title=Mahalanobis distance with radial basis function network on protein secondary structures
|journal = Engineering in Medicine and Biology Society, Proceedings of the Annual International Conference of the IEEE|isbn=0-7803-7612-9
| |||