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