RBFinit function

Initialization of parameters for a Radial Basis Function classifier

RBFinit returns initial parameter values for a Radial Basis Function classifier.

RBFinit(x, y, nproto)

Arguments

• x: Input matrix of size n x d, where n is the number of objects and d the number of attributes.
• y: Vector of class labels (of length n). May be a factor, or a vector of integers from 1 to M (number of classes).
• nproto: Number of prototypes

Returns

A list with three elements containing the initialized network parameters

• P: Matrix of size (R,d), containing the R prototype coordinates.
• Gamma: Vector of length R, containing the scale parameters.
• W: Matrix of size (R,M), containing the hidden-to-output weights.

Details

The prototypes are initialized by the k-means algorithms. The hidden-to-output weights are initialized by linear regression. The scale parameter for each prototype is computed as the inverse of the square root of the mean squared distances to this prototype. The final number of prototypes may be different from the desired number nproto depending on the result of the k-means clustering (clusters composed of only one input vector are discarded).

Examples

## Glass dataset
data(glass)
xapp<-glass$x[1:89,]
yapp<-glass$y[1:89]
param0<-RBFinit(xapp,yapp,nproto=7)
param0

See Also

RBFfit, RBFval

Author(s)

Thierry Denoeux.