RBFfit function

Training of a radial basis function classifier

RBFfit performs parameter optimization for a radial basis function (RBF) classifier.

RBFfit(
  x,
  y,
  param,
  lambda = 0,
  control = list(fnscale = -1, trace = 2, maxit = 1000),
  optimProto = TRUE
)

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).
• param: Initial parameters (see RBFinit).
• lambda: Regularization hyperparameter (default=0).
• control: Parameters passed to function optim.
• optimProto: Boolean. If TRUE, the prototypes are optimized (default). Otherwise, they are fixed.

Returns

A list with three elements:

• param: Optimized network parameters.
• loglik: Final value of the log-likelihood objective function.
• err: Training error rate.

Details

The RBF neural network is trained by maximizing the conditional log-likelihood (or, equivalently, by minimizing the cross-entropy loss function). The optimization procedure is the BFGS algorithm implemented in function optim.

Examples

## Glass dataset
data(glass)
xapp<-glass$x[1:89,]
yapp<-glass$y[1:89]
## Initialization
param0<-RBFinit(xapp,yapp,nproto=7)
## Training
fit<-RBFfit(xapp,yapp,param0,control=list(fnscale=-1,trace=2))

See Also

proDSinit, proDSval

Author(s)

Thierry Denoeux.