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