wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual glmevfwd wolffd@0: wolffd@0: wolffd@0: wolffd@0:

glmevfwd wolffd@0:

wolffd@0:

wolffd@0: Purpose wolffd@0:

wolffd@0: Forward propagation with evidence for GLM wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

wolffd@0:

wolffd@0: 
wolffd@0: [y, extra] = glmevfwd(net, x, t, x_test)
wolffd@0: [y, extra, invhess] = glmevfwd(net, x, t, x_test, invhess)
wolffd@0:

wolffd@0: wolffd@0: wolffd@0:

wolffd@0: Description wolffd@0:

wolffd@0: y = glmevfwd(net, x, t, x_test) takes a network data structure wolffd@0: net together with the input x and target t training data wolffd@0: and input test data x_test. wolffd@0: It returns the normal forward propagation through the network y wolffd@0: together with a matrix extra which consists of error bars (variance) wolffd@0: for a regression problem or moderated outputs for a classification problem. wolffd@0: wolffd@0:

The optional argument (and return value) wolffd@0: invhess is the inverse of the network Hessian wolffd@0: computed on the training data inputs and targets. Passing it in avoids wolffd@0: recomputing it, which can be a significant saving for large training sets. wolffd@0: wolffd@0:

wolffd@0: See Also wolffd@0:

wolffd@0: fevbayes

wolffd@0: Pages: wolffd@0: Index wolffd@0:

wolffd@0: