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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual glmevfwd
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> glmevfwd
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Forward propagation with evidence for GLM
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 [y, extra] = glmevfwd(net, x, t, x_test)
wolffd@0 21 [y, extra, invhess] = glmevfwd(net, x, t, x_test, invhess)
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>y = glmevfwd(net, x, t, x_test)</CODE> takes a network data structure
wolffd@0 29 <CODE>net</CODE> together with the input <CODE>x</CODE> and target <CODE>t</CODE> training data
wolffd@0 30 and input test data <CODE>x_test</CODE>.
wolffd@0 31 It returns the normal forward propagation through the network <CODE>y</CODE>
wolffd@0 32 together with a matrix <CODE>extra</CODE> which consists of error bars (variance)
wolffd@0 33 for a regression problem or moderated outputs for a classification problem.
wolffd@0 34
wolffd@0 35 <p>The optional argument (and return value)
wolffd@0 36 <CODE>invhess</CODE> is the inverse of the network Hessian
wolffd@0 37 computed on the training data inputs and targets. Passing it in avoids
wolffd@0 38 recomputing it, which can be a significant saving for large training sets.
wolffd@0 39
wolffd@0 40 <p><h2>
wolffd@0 41 See Also
wolffd@0 42 </h2>
wolffd@0 43 <CODE><a href="fevbayes.htm">fevbayes</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>