📄 rbfprior.htm
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<html><head><title>Netlab Reference Manual rbfprior</title></head><body><H1> rbfprior</H1><h2>Purpose</h2>Create Gaussian prior and output layer mask for RBF.<p><h2>Synopsis</h2><PRE>[mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2)</PRE><p><h2>Description</h2><CODE>[mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2)</CODE> generates a vector<CODE>mask</CODE> that selects only the outputlayer weights. This is because most uses of RBF networks in a Bayesiancontext have fixed basis functions with the output layer as the onlyadjustable parameters. In particular, the Neuroscale output error functionis designed to work only with this mask.<p>The return value<CODE>prior</CODE> is a data structure, with fields <CODE>prior.alpha</CODE> and <CODE>prior.index</CODE>, whichspecifies a Gaussian prior distribution for the network weights in anRBF network. The parameters <CODE>aw2</CODE> and <CODE>ab2</CODE> are allscalars and represent the regularization coefficients for two groupsof parameters in the network corresponding to second-layer weights, and second-layer biasesrespectively. Then <CODE>prior.alpha</CODE> represents a column vector oflength 2 containing the parameters, and <CODE>prior.index</CODE> is a matrixspecifying which weights belong in each group. Each column has oneelement for each weight in the matrix, using the standard ordering asdefined in <CODE>rbfpak</CODE>, and each element is 1 or 0 according towhether the weight is a member of the corresponding group or not. <p><h2>See Also</h2><CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="evidence.htm">evidence</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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