Mercurial > hg > camir-aes2014

diff toolboxes/FullBNT-1.0.7/nethelp3.3/mlpprior.htm @ 0:e9a9cd732c1e tip

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
parents
children
line wrap: on
line diff
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlpprior.htm	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,58 @@
+<html>
+<head>
+<title>
+Netlab Reference Manual mlpprior
+</title>
+</head>
+<body>
+<H1> mlpprior
+</H1>
+<h2>
+Purpose
+</h2>
+Create Gaussian prior for mlp.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)</PRE>
+
+
+<p><h2>
+Description
+</h2>
+<CODE>prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)</CODE> 
+generates a data structure
+<CODE>prior</CODE>, with fields <CODE>prior.alpha</CODE> and <CODE>prior.index</CODE>, which
+specifies a Gaussian prior distribution for the network weights in a
+two-layer feedforward network. Two different cases are possible. In
+the first case, <CODE>aw1</CODE>, <CODE>ab1</CODE>, <CODE>aw2</CODE> and <CODE>ab2</CODE> are all
+scalars and represent the regularization coefficients for four groups
+of parameters in the network corresponding to first-layer weights,
+first-layer biases, second-layer weights, and second-layer biases
+respectively. Then <CODE>prior.alpha</CODE> represents a column vector of
+length 4 containing the parameters, and <CODE>prior.index</CODE> is a matrix
+specifying which weights belong in each group. Each column has one
+element for each weight in the matrix, using the standard ordering as
+defined in <CODE>mlppak</CODE>, and each element is 1 or 0 according to
+whether the weight is a member of the corresponding group or not.  In
+the second case the parameter <CODE>aw1</CODE> is a vector of length equal to
+the number of inputs in the network, and the corresponding matrix
+<CODE>prior.index</CODE> now partitions the first-layer weights into groups
+corresponding to the weights fanning out of each input unit. This 
+prior is appropriate for the technique of automatic relevance
+determination. 
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</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