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authorziejd22017-09-28 15:04:40 -0500
committerziejd22017-09-28 15:04:40 -0500
commit8070dc963753142bb86c4ed698d91fd623ed28e7 (patch)
treed0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/demev1.htm
parent7cc31810d53176e805532b2789955f4eedbce6bb (diff)
downloadBNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02
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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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