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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/demglm1.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/demglm1.htm b/sourcecodes/bnt-master/nethelp3.3/demglm1.htm new file mode 100644 index 00000000..7740b361 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/demglm1.htm @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual demglm1 +</title> +</head> +<body> +<H1> demglm1 +</H1> +<h2> +Purpose +</h2> +Demonstrate simple classification using a generalized linear model. + +<p><h2> +Synopsis +</h2> +<PRE> +demglm1</PRE> + + +<p><h2> +Description +</h2> + +The problem consists of a two dimensional input +matrix <CODE>data</CODE> and a vector of classifications <CODE>t</CODE>. The data is +generated from two Gaussian clusters, and a generalized linear model +with logistic output is trained using iterative reweighted least squares. +A plot of the data together with the 0.1, 0.5 and 0.9 contour lines +of the conditional probability is generated. + +<p><h2> +See Also +</h2> +<CODE><a href="demglm2.htm">demglm2</a></CODE>, <CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</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 |