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Netlab Reference Manual glmevfwd
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<H1> glmevfwd
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<h2>
Purpose
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Forward propagation with evidence for GLM

<p><h2>
Synopsis
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<PRE>

[y, extra] = glmevfwd(net, x, t, x_test)
[y, extra, invhess] = glmevfwd(net, x, t, x_test, invhess)
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<p><h2>
Description
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<CODE>y = glmevfwd(net, x, t, x_test)</CODE> takes a network data structure
<CODE>net</CODE> together with the input <CODE>x</CODE> and target <CODE>t</CODE> training data
and input test data <CODE>x_test</CODE>.
It returns the normal forward propagation through the network <CODE>y</CODE>
together with a matrix <CODE>extra</CODE> which consists of error bars (variance)
for a regression problem or moderated outputs for a classification problem.

<p>The optional argument (and return value)
<CODE>invhess</CODE> is the inverse of the network Hessian
computed on the training data inputs and targets.  Passing it in avoids
recomputing it, which can be a significant saving for large training sets.

<p><h2>
See Also
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<CODE><a href="fevbayes.htm">fevbayes</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
<hr>
<p>Copyright (c) Ian T Nabney (1996-9)


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author	wolffd
date	Tue, 10 Feb 2015 15:05:51 +0000
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