Content deleted Content added
Fix source to prevent this article from confounding maintenance processes |
Citation bot (talk | contribs) Alter: template type. | Use this bot. Report bugs. | Suggested by AManWithNoPlan | #UCB_webform 717/1682 |
||
Line 31:
:<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
| |||