h = glmhess(net, x, t) [h, hdata] = glmhess(net, x, t) h = glmhess(net, x, t, hdata)
h = glmhess(net, x, t) takes a GLM network data structure net,
a matrix x of input values, and a matrix t of target
values and returns the full Hessian matrix h corresponding to
the second derivatives of the negative log posterior distribution,
evaluated for the current weight and bias values as defined by
net. Note that the target data is not required in the calculation,
but is included to make the interface uniform with nethess. For
linear and logistic outputs, the computation is very simple and is
done (in effect) in one line in glmtrain.
[h, hdata] = glmhess(net, x, t) returns both the Hessian matrix
h and the contribution hdata arising from the data dependent
term in the Hessian.
h = glmhess(net, x, t, hdata) takes a network data structure
net, a matrix x of input values, and a matrix t of
target values, together with the contribution hdata arising from
the data dependent term in the Hessian, and returns the full Hessian
matrix h corresponding to the second derivatives of the negative
log posterior distribution. This version saves computation time if
hdata has already been evaluated for the current weight and bias
values.
glmtrain to take a Newton step for
softmax outputs.
Hessian = glmhess(net, x, t); deltaw = -gradient*pinv(Hessian);
glm, glmtrain, hesschek, nethessCopyright (c) Ian T Nabney (1996-9)