prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)
prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)
generates a data structure
prior, with fields prior.alpha and prior.index, which
specifies a Gaussian prior distribution for the network weights in a
two-layer feedforward network. Two different cases are possible. In
the first case, aw1, ab1, aw2 and ab2 are all
scalars and represent the regularization coefficients for four groups
of parameters in the network corresponding to first-layer weights,
first-layer biases, second-layer weights, and second-layer biases
respectively. Then prior.alpha represents a column vector of
length 4 containing the parameters, and prior.index is a matrix
specifying which weights belong in each group. Each column has one
element for each weight in the matrix, using the standard ordering as
defined in mlppak, and each element is 1 or 0 according to
whether the weight is a member of the corresponding group or not. In
the second case the parameter aw1 is a vector of length equal to
the number of inputs in the network, and the corresponding matrix
prior.index now partitions the first-layer weights into groups
corresponding to the weights fanning out of each input unit. This
prior is appropriate for the technique of automatic relevance
determination.
mlp, mlperr, mlpgrad, evidenceCopyright (c) Ian T Nabney (1996-9)