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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 relevancedetermination (ARD) using a synthetic problem having three inputvariables: <CODE>x1</CODE> is sampled uniformly from the range (0,1) and hasa 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 randomlyfrom a Gaussian distribution. The single target variable is determinedby <CODE>sin(2*pi*x1)</CODE> with additive Gaussian noise. Thus <CODE>x1</CODE> isvery relevant for determining the target value, <CODE>x2</CODE> is of somerelevance, while <CODE>x3</CODE> is irrelevant. The prior over weights isgiven by the ARD Gaussian prior with a separate hyper-parameter forthe group of weights associated with each input. A multi-layerperceptron is trained on this data, with re-estimation of thehyper-parameters using <CODE>evidence</CODE>. The final values for thehyper-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>⌨️ 快捷键说明
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