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author Daniel Wolff
date Fri, 19 Aug 2016 13:07:06 +0200
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Daniel@0 1 <html>
Daniel@0 2 <head>
Daniel@0 3 <title>
Daniel@0 4 Netlab Reference Manual glmgrad
Daniel@0 5 </title>
Daniel@0 6 </head>
Daniel@0 7 <body>
Daniel@0 8 <H1> glmgrad
Daniel@0 9 </H1>
Daniel@0 10 <h2>
Daniel@0 11 Purpose
Daniel@0 12 </h2>
Daniel@0 13 Evaluate gradient of error function for generalized linear model.
Daniel@0 14
Daniel@0 15 <p><h2>
Daniel@0 16 Synopsis
Daniel@0 17 </h2>
Daniel@0 18 <PRE>
Daniel@0 19
Daniel@0 20 g = glmgrad(net, x, t)
Daniel@0 21 [g, gdata, gprior] = glmgrad(net, x, t)
Daniel@0 22 </PRE>
Daniel@0 23
Daniel@0 24
Daniel@0 25 <p><h2>
Daniel@0 26 Description
Daniel@0 27 </h2>
Daniel@0 28 <CODE>g = glmgrad(net, x, t)</CODE> takes a generalized linear model
Daniel@0 29 data structure <CODE>net</CODE>
Daniel@0 30 together with a matrix <CODE>x</CODE> of input vectors and a matrix <CODE>t</CODE>
Daniel@0 31 of target vectors, and evaluates the gradient <CODE>g</CODE> of the error
Daniel@0 32 function with respect to the network weights. The error function
Daniel@0 33 corresponds to the choice of output unit activation function. Each row
Daniel@0 34 of <CODE>x</CODE> corresponds to one input vector and each row of <CODE>t</CODE>
Daniel@0 35 corresponds to one target vector.
Daniel@0 36
Daniel@0 37 <p><CODE>[g, gdata, gprior] = glmgrad(net, x, t)</CODE> also returns separately
Daniel@0 38 the data and prior contributions to the gradient.
Daniel@0 39
Daniel@0 40 <p><h2>
Daniel@0 41 See Also
Daniel@0 42 </h2>
Daniel@0 43 <CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="glmfwd.htm">glmfwd</a></CODE>, <CODE><a href="glmerr.htm">glmerr</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE><hr>
Daniel@0 44 <b>Pages:</b>
Daniel@0 45 <a href="index.htm">Index</a>
Daniel@0 46 <hr>
Daniel@0 47 <p>Copyright (c) Ian T Nabney (1996-9)
Daniel@0 48
Daniel@0 49
Daniel@0 50 </body>
Daniel@0 51 </html>