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1 <html>
2 <head>
3 <title>
4 Netlab Reference Manual demev2
5 </title>
6 </head>
7 <body>
8 <H1> demev2
9 </H1>
10 <h2>
11 Purpose
12 </h2>
13 Demonstrate Bayesian classification for the MLP.
14
15 <p><h2>
16 Synopsis
17 </h2>
18 <PRE>
19 demev2</PRE>
20
21
22 <p><h2>
23 Description
24 </h2>
25 A synthetic two class two-dimensional dataset <CODE>x</CODE> is sampled
26 from a mixture of four Gaussians. Each class is
27 associated with two of the Gaussians so that the optimal decision
28 boundary is non-linear.
29 A 2-layer
30 network with logistic outputs is trained by minimizing the cross-entropy
31 error function with isotroipc Gaussian regularizer (one hyperparameter for
32 each of the four standard weight groups), using the scaled
33 conjugate gradient optimizer. The hyperparameter vectors <CODE>alpha</CODE> and
34 <CODE>beta</CODE> are re-estimated using the function <CODE>evidence</CODE>. A graph
35 is plotted of the optimal, regularised, and unregularised decision
36 boundaries. A further plot of the moderated versus unmoderated contours
37 is generated.
38
39 <p><h2>
40 See Also
41 </h2>
42 <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="demmlp2.htm">demmlp2</a></CODE><hr>
43 <b>Pages:</b>
44 <a href="index.htm">Index</a>
45 <hr>
46 <p>Copyright (c) Ian T Nabney (1996-9)
47
48
49 </body>
50 </html>