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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
|---|---|---|
| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/rbfprior.htm | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz | |
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02
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diff --git a/sourcecodes/bnt-master/nethelp3.3/rbfprior.htm b/sourcecodes/bnt-master/nethelp3.3/rbfprior.htm new file mode 100644 index 00000000..202bdde7 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/rbfprior.htm @@ -0,0 +1,59 @@ +<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 output +layer weights. This is because most uses of RBF networks in a Bayesian +context have fixed basis functions with the output layer as the only +adjustable parameters. In particular, the Neuroscale output error function +is 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>, which +specifies a Gaussian prior distribution for the network weights in an +RBF network. The parameters <CODE>aw2</CODE> and <CODE>ab2</CODE> are all +scalars and represent the regularization coefficients for two groups +of parameters in the network corresponding to + second-layer weights, and second-layer biases +respectively. Then <CODE>prior.alpha</CODE> represents a column vector of +length 2 containing the parameters, and <CODE>prior.index</CODE> is a matrix +specifying which weights belong in each group. Each column has one +element for each weight in the matrix, using the standard ordering as +defined in <CODE>rbfpak</CODE>, and each element is 1 or 0 according to +whether 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> \ No newline at end of file |