Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual glmgrad Daniel@0: Daniel@0: Daniel@0: Daniel@0:

glmgrad Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Evaluate gradient of error function for generalized linear model. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

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Daniel@0: 
Daniel@0: g = glmgrad(net, x, t)
Daniel@0: [g, gdata, gprior] = glmgrad(net, x, t)
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Daniel@0: Description Daniel@0:

Daniel@0: g = glmgrad(net, x, t) takes a generalized linear model Daniel@0: data structure net Daniel@0: together with a matrix x of input vectors and a matrix t Daniel@0: of target vectors, and evaluates the gradient g of the error Daniel@0: function with respect to the network weights. The error function Daniel@0: corresponds to the choice of output unit activation function. Each row Daniel@0: of x corresponds to one input vector and each row of t Daniel@0: corresponds to one target vector. Daniel@0: Daniel@0:

[g, gdata, gprior] = glmgrad(net, x, t) also returns separately Daniel@0: the data and prior contributions to the gradient. Daniel@0: Daniel@0:

Daniel@0: See Also Daniel@0:

Daniel@0: glm, glmpak, glmunpak, glmfwd, glmerr, glmtrain

Daniel@0: Pages: Daniel@0: Index Daniel@0:

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