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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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<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>