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<html><head><title>Netlab Reference Manual fevbayes</title></head><body><H1> fevbayes</H1><h2>Purpose</h2>Evaluate Bayesian regularisation for network forward propagation.<p><h2>Synopsis</h2><PRE>extra = fevbayes(net, y, a, x, t, x_test)[extra, invhess] = fevbayes(net, y, a, x, t, x_test, invhess)</PRE><p><h2>Description</h2><CODE>extra = fevbayes(net, y, a, x, t, x_test)</CODE> takes a network data structure <CODE>net</CODE> together with a set of hidden unit activations <CODE>a</CODE> from test inputs <CODE>x_test</CODE>, training data inputs <CODE>x</CODE> and <CODE>t</CODE> andoutputs a matrix of extra information <CODE>extra</CODE> that consists oferror bars (variance)for a regression problem or moderated outputs for a classification problem.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>This is called by network-specific functions such as <CODE>mlpevfwd</CODE> whichare needed since the return values (predictions and hidden unit activations)for different network types are in different orders (for good reasons).<p><h2>See Also</h2><CODE><a href="mlpevfwd.htm">mlpevfwd</a></CODE>, <CODE><a href="rbfevfwd.htm">rbfevfwd</a></CODE>, <CODE><a href="glmevfwd.htm">glmevfwd</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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