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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/glmhess.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/glmhess.htm b/sourcecodes/bnt-master/nethelp3.3/glmhess.htm new file mode 100644 index 00000000..481e3220 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/glmhess.htm @@ -0,0 +1,74 @@ +<html> +<head> +<title> +Netlab Reference Manual glmhess +</title> +</head> +<body> +<H1> glmhess +</H1> +<h2> +Purpose +</h2> +Evaluate the Hessian matrix for a generalised linear model. + +<p><h2> +Synopsis +</h2> +<PRE> +h = glmhess(net, x, t) +[h, hdata] = glmhess(net, x, t) +h = glmhess(net, x, t, hdata) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>h = glmhess(net, x, t)</CODE> takes a GLM network data structure <CODE>net</CODE>, +a matrix <CODE>x</CODE> of input values, and a matrix <CODE>t</CODE> of target +values and returns the full Hessian matrix <CODE>h</CODE> corresponding to +the second derivatives of the negative log posterior distribution, +evaluated for the current weight and bias values as defined by +<CODE>net</CODE>. Note that the target data is not required in the calculation, +but is included to make the interface uniform with <CODE>nethess</CODE>. For +linear and logistic outputs, the computation is very simple and is +done (in effect) in one line in <CODE>glmtrain</CODE>. + +<p><CODE>[h, hdata] = glmhess(net, x, t)</CODE> returns both the Hessian matrix +<CODE>h</CODE> and the contribution <CODE>hdata</CODE> arising from the data dependent +term in the Hessian. + +<p><CODE>h = glmhess(net, x, t, hdata)</CODE> takes a network data structure +<CODE>net</CODE>, a matrix <CODE>x</CODE> of input values, and a matrix <CODE>t</CODE> of +target values, together with the contribution <CODE>hdata</CODE> arising from +the data dependent term in the Hessian, and returns the full Hessian +matrix <CODE>h</CODE> corresponding to the second derivatives of the negative +log posterior distribution. This version saves computation time if +<CODE>hdata</CODE> has already been evaluated for the current weight and bias +values. + +<p><h2> +Example +</h2> +The Hessian matrix is used by <CODE>glmtrain</CODE> to take a Newton step for +softmax outputs. +<PRE> + +Hessian = glmhess(net, x, t); +deltaw = -gradient*pinv(Hessian); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE>, <CODE><a href="hesschek.htm">hesschek</a></CODE>, <CODE><a href="nethess.htm">nethess</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 |