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+<html>
+<head>
+<title>
+Netlab Reference Manual mlpinit
+</title>
+</head>
+<body>
+<H1> mlpinit
+</H1>
+<h2>
+Purpose
+</h2>
+Initialise the weights in a 2-layer feedforward network.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+net = mlpinit(net, prior)
+</PRE>
+
+
+<p><h2>
+Description
+</h2>
+
+<p><CODE>net = mlpinit(net, prior)</CODE> takes a 2-layer feedforward network
+<CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian
+distribution. If <CODE>prior</CODE> is a scalar, then all of the parameters
+(weights and biases) are sampled from a single isotropic Gaussian with
+inverse variance equal to <CODE>prior</CODE>. If <CODE>prior</CODE> is a data
+structure of the kind generated by <CODE>mlpprior</CODE>, then the parameters
+are sampled from multiple Gaussians according to their groupings
+(defined by the <CODE>index</CODE> field) with corresponding variances
+(defined by the <CODE>alpha</CODE> field).
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlpprior.htm">mlpprior</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</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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