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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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1 <html> | |
2 <head> | |
3 <title> | |
4 Netlab Reference Manual demard | |
5 </title> | |
6 </head> | |
7 <body> | |
8 <H1> demard | |
9 </H1> | |
10 <h2> | |
11 Purpose | |
12 </h2> | |
13 Automatic relevance determination using the MLP. | |
14 | |
15 <p><h2> | |
16 Synopsis | |
17 </h2> | |
18 <PRE> | |
19 demmlp1</PRE> | |
20 | |
21 | |
22 <p><h2> | |
23 Description | |
24 </h2> | |
25 This script demonstrates the technique of automatic relevance | |
26 determination (ARD) using a synthetic problem having three input | |
27 variables: <CODE>x1</CODE> is sampled uniformly from the range (0,1) and has | |
28 a low level of added Gaussian noise, <CODE>x2</CODE> is a copy of <CODE>x1</CODE> | |
29 with a higher level of added noise, and <CODE>x3</CODE> is sampled randomly | |
30 from a Gaussian distribution. The single target variable is determined | |
31 by <CODE>sin(2*pi*x1)</CODE> with additive Gaussian noise. Thus <CODE>x1</CODE> is | |
32 very relevant for determining the target value, <CODE>x2</CODE> is of some | |
33 relevance, while <CODE>x3</CODE> is irrelevant. The prior over weights is | |
34 given by the ARD Gaussian prior with a separate hyper-parameter for | |
35 the group of weights associated with each input. A multi-layer | |
36 perceptron is trained on this data, with re-estimation of the | |
37 hyper-parameters using <CODE>evidence</CODE>. The final values for the | |
38 hyper-parameters reflect the relative importance of the three inputs. | |
39 | |
40 <p><h2> | |
41 See Also | |
42 </h2> | |
43 <CODE><a href="demmlp1.htm">demmlp1</a></CODE>, <CODE><a href="demev1.htm">demev1</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE><hr> | |
44 <b>Pages:</b> | |
45 <a href="index.htm">Index</a> | |
46 <hr> | |
47 <p>Copyright (c) Ian T Nabney (1996-9) | |
48 | |
49 | |
50 </body> | |
51 </html> |