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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/rbftrain.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/rbftrain.htm b/sourcecodes/bnt-master/nethelp3.3/rbftrain.htm new file mode 100644 index 00000000..04c2bef5 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/rbftrain.htm @@ -0,0 +1,90 @@ +<html> +<head> +<title> +Netlab Reference Manual rbftrain +</title> +</head> +<body> +<H1> rbftrain +</H1> +<h2> +Purpose +</h2> +Two stage training of RBF network. + +<p><h2> +Description +</h2> +<CODE>net = rbftrain(net, options, x, t)</CODE> uses a +two stage training +algorithm to set the weights in the RBF model structure <CODE>net</CODE>. +Each row of <CODE>x</CODE> corresponds to one +input vector and each row of <CODE>t</CODE> contains the corresponding target vector. +The centres are determined by fitting a Gaussian mixture model +with circular covariances using the EM algorithm through a call to +<CODE>rbfsetbf</CODE>. (The mixture model is +initialised using a small number of iterations of the K-means algorithm.) +If the activation functions are Gaussians, then the basis function widths +are then set to the maximum inter-centre squared distance. + +<p>For linear outputs, +the hidden to output +weights that give rise to the least squares solution +can then be determined using the pseudo-inverse. For neuroscale outputs, +the hidden to output weights are determined using the iterative shadow +targets algorithm. + Although this two stage +procedure may not give solutions with as low an error as using general +purpose non-linear optimisers, it is much faster. + +<p>The options vector may have two rows: if this is the case, then the second row +is passed to <CODE>rbfsetbf</CODE>, which allows the user to specify a different +number iterations for RBF and GMM training. +The optional parameters to <CODE>rbftrain</CODE> have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values during EM training. + +<p><CODE>options(2)</CODE> is a measure of the precision required for the value +of the weights <CODE>w</CODE> at the solution. + +<p><CODE>options(3)</CODE> is a measure of the precision required of the objective +function at the solution. Both this and the previous condition must be +satisfied for termination. + +<p><CODE>options(5)</CODE> is set to 1 if the basis functions parameters should remain +unchanged; default 0. + +<p><CODE>options(6)</CODE> is set to 1 if the output layer weights should be should +set using PCA. This is only relevant for Neuroscale outputs; default 0. + +<p><CODE>options(14)</CODE> is the maximum number of iterations for the shadow +targets algorithm; +default 100. + +<p><h2> +Example +</h2> +The following example creates an RBF network and then trains it: +<PRE> + +net = rbf(1, 4, 1, 'gaussian'); +options(1, :) = foptions; +options(2, :) = foptions; +options(2, 14) = 10; % 10 iterations of EM +options(2, 5) = 1; % Check for covariance collapse in EM +net = rbftrain(net, options, x, t); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE>, <CODE><a href="rbfsetbf.htm">rbfsetbf</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 |