📄 glmevfwd.htm
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<html><head><title>Netlab Reference Manual glmevfwd</title></head><body><H1> glmevfwd</H1><h2>Purpose</h2>Forward propagation with evidence for GLM<p><h2>Synopsis</h2><PRE>[y, extra] = glmevfwd(net, x, t, x_test)[y, extra, invhess] = glmevfwd(net, x, t, x_test, invhess)</PRE><p><h2>Description</h2><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 dataand 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 Hessiancomputed on the training data inputs and targets. Passing it in avoidsrecomputing it, which can be a significant saving for large training sets.<p><h2>See Also</h2><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)</body></html>⌨️ 快捷键说明
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