Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/nethelp3.3/demev2.htm @ 0:e9a9cd732c1e tip
first hg version after svn
| author | wolffd |
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| date | Tue, 10 Feb 2015 15:05:51 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demev2.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual demev2 +</title> +</head> +<body> +<H1> demev2 +</H1> +<h2> +Purpose +</h2> +Demonstrate Bayesian classification for the MLP. + +<p><h2> +Synopsis +</h2> +<PRE> +demev2</PRE> + + +<p><h2> +Description +</h2> +A synthetic two class two-dimensional dataset <CODE>x</CODE> is sampled +from a mixture of four Gaussians. Each class is +associated with two of the Gaussians so that the optimal decision +boundary is non-linear. +A 2-layer +network with logistic outputs is trained by minimizing the cross-entropy +error function with isotroipc Gaussian regularizer (one hyperparameter for +each of the four standard weight groups), using the scaled +conjugate gradient optimizer. The hyperparameter vectors <CODE>alpha</CODE> and +<CODE>beta</CODE> are re-estimated using the function <CODE>evidence</CODE>. A graph +is plotted of the optimal, regularised, and unregularised decision +boundaries. A further plot of the moderated versus unmoderated contours +is generated. + +<p><h2> +See Also +</h2> +<CODE><a href="evidence.htm">evidence</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE>, <CODE><a href="demard.htm">demard</a></CODE>, <CODE><a href="demmlp2.htm">demmlp2</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