wolffd@0: wolffd@0:
wolffd@0:wolffd@0: e = rbferr(net, x, t) wolffd@0: [e, edata, eprior] = rbferr(net, x, t) wolffd@0:wolffd@0: wolffd@0: wolffd@0:
e = rbferr(net, x, t)
takes a network data structure net
together
wolffd@0: with a matrix x
of input
wolffd@0: vectors and a matrix t
of target vectors, and evaluates the
wolffd@0: appropriate error function e
depending on net.outfn
.
wolffd@0: Each row of x
corresponds to one
wolffd@0: input vector and each row of t
contains the corresponding target vector.
wolffd@0:
wolffd@0: [e, edata, eprior] = rbferr(net, x, t)
additionally returns the
wolffd@0: data and prior components of the error, assuming a zero mean Gaussian
wolffd@0: prior on the weights with inverse variance parameters alpha
and
wolffd@0: beta
taken from the network data structure net
.
wolffd@0:
wolffd@0:
rbf
, rbffwd
, rbfgrad
, rbfpak
, rbftrain
, rbfunpak
Copyright (c) Ian T Nabney (1996-9) wolffd@0: wolffd@0: wolffd@0: wolffd@0: