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1 <html>
2 <head>
3 <title>
4 Netlab Reference Manual demev3
5 </title>
6 </head>
7 <body>
8 <H1> demev3
9 </H1>
10 <h2>
11 Purpose
12 </h2>
13 Demonstrate Bayesian regression for the RBF.
14
15 <p><h2>
16 Synopsis
17 </h2>
18 <PRE>
19 demev3</PRE>
20
21
22 <p><h2>
23 Description
24 </h2>
25 The problem consists an input variable <CODE>x</CODE> which sampled from a
26 Gaussian distribution, and a target variable <CODE>t</CODE> generated by
27 computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian noise. An RBF
28 network with linear outputs is trained by minimizing a sum-of-squares
29 error function with isotropic Gaussian regularizer, using the scaled
30 conjugate gradient optimizer. The hyperparameters <CODE>alpha</CODE> and
31 <CODE>beta</CODE> are re-estimated using the function <CODE>evidence</CODE>. A graph
32 is plotted of the original function, the training data, the trained
33 network function, and the error bars.
34
35 <p><h2>
36 See Also
37 </h2>
38 <CODE><a href="demev1.htm">demev1</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE>, <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE>, <CODE><a href="netevfwd.htm">netevfwd</a></CODE><hr>
39 <b>Pages:</b>
40 <a href="index.htm">Index</a>
41 <hr>
42 <p>Copyright (c) Ian T Nabney (1996-9)
43
44
45 </body>
46 </html>