Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/nethelp3.3/demard.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/demard.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +<html> +<head> +<title> +Netlab Reference Manual demard +</title> +</head> +<body> +<H1> demard +</H1> +<h2> +Purpose +</h2> +Automatic relevance determination using the MLP. + +<p><h2> +Synopsis +</h2> +<PRE> +demmlp1</PRE> + + +<p><h2> +Description +</h2> +This script demonstrates the technique of automatic relevance +determination (ARD) using a synthetic problem having three input +variables: <CODE>x1</CODE> is sampled uniformly from the range (0,1) and has +a low level of added Gaussian noise, <CODE>x2</CODE> is a copy of <CODE>x1</CODE> +with a higher level of added noise, and <CODE>x3</CODE> is sampled randomly +from a Gaussian distribution. The single target variable is determined +by <CODE>sin(2*pi*x1)</CODE> with additive Gaussian noise. Thus <CODE>x1</CODE> is +very relevant for determining the target value, <CODE>x2</CODE> is of some +relevance, while <CODE>x3</CODE> is irrelevant. The prior over weights is +given by the ARD Gaussian prior with a separate hyper-parameter for +the group of weights associated with each input. A multi-layer +perceptron is trained on this data, with re-estimation of the +hyper-parameters using <CODE>evidence</CODE>. The final values for the +hyper-parameters reflect the relative importance of the three inputs. + +<p><h2> +See Also +</h2> +<CODE><a href="demmlp1.htm">demmlp1</a></CODE>, <CODE><a href="demev1.htm">demev1</a></CODE>, <CODE><a href="mlp.htm">mlp</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