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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
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| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/mlpprior.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/mlpprior.htm b/sourcecodes/bnt-master/nethelp3.3/mlpprior.htm new file mode 100644 index 00000000..1821e919 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/mlpprior.htm @@ -0,0 +1,58 @@ +<html> +<head> +<title> +Netlab Reference Manual mlpprior +</title> +</head> +<body> +<H1> mlpprior +</H1> +<h2> +Purpose +</h2> +Create Gaussian prior for mlp. + +<p><h2> +Synopsis +</h2> +<PRE> +prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)</PRE> + + +<p><h2> +Description +</h2> +<CODE>prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)</CODE> +generates a data structure +<CODE>prior</CODE>, with fields <CODE>prior.alpha</CODE> and <CODE>prior.index</CODE>, which +specifies a Gaussian prior distribution for the network weights in a +two-layer feedforward network. Two different cases are possible. In +the first case, <CODE>aw1</CODE>, <CODE>ab1</CODE>, <CODE>aw2</CODE> and <CODE>ab2</CODE> are all +scalars and represent the regularization coefficients for four groups +of parameters in the network corresponding to first-layer weights, +first-layer biases, second-layer weights, and second-layer biases +respectively. Then <CODE>prior.alpha</CODE> represents a column vector of +length 4 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>mlppak</CODE>, and each element is 1 or 0 according to +whether the weight is a member of the corresponding group or not. In +the second case the parameter <CODE>aw1</CODE> is a vector of length equal to +the number of inputs in the network, and the corresponding matrix +<CODE>prior.index</CODE> now partitions the first-layer weights into groups +corresponding to the weights fanning out of each input unit. This +prior is appropriate for the technique of automatic relevance +determination. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</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 |