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+<html>
+<head>
+<title>
+Netlab Reference Manual demev1
+</title>
+</head>
+<body>
+<H1> demev1
+</H1>
+<h2>
+Purpose
+</h2>
+Demonstrate Bayesian regression for the MLP.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+demev1</PRE>
+
+
+<p><h2>
+Description
+</h2>
+The problem consists an input variable <CODE>x</CODE> which sampled from a
+Gaussian distribution, and a target variable <CODE>t</CODE> generated by
+computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian noise. A 2-layer
+network with linear outputs is trained by minimizing a sum-of-squares
+error function with isotropic Gaussian regularizer, using the scaled
+conjugate gradient optimizer. The hyperparameters <CODE>alpha</CODE> and
+<CODE>beta</CODE> are re-estimated using the function <CODE>evidence</CODE>. A graph 
+is plotted of the original function, the training data, the trained
+network function, and the error bars.
+
+<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="demmlp1.htm">demmlp1</a></CODE><hr>
+<b>Pages:</b>
+<a href="index.htm">Index</a>
+<hr>
+<p>Copyright (c) Ian T Nabney (1996-9)
+
+
+</body>
+</html>
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