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1 <html>
2 <head>
3 <title>
4 Netlab Reference Manual rbfprior
5 </title>
6 </head>
7 <body>
8 <H1> rbfprior
9 </H1>
10 <h2>
11 Purpose
12 </h2>
13 Create Gaussian prior and output layer mask for RBF.
14
15 <p><h2>
16 Synopsis
17 </h2>
18 <PRE>
19 [mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2)</PRE>
20
21
22 <p><h2>
23 Description
24 </h2>
25 <CODE>[mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2)</CODE>
26 generates a vector
27 <CODE>mask</CODE> that selects only the output
28 layer weights. This is because most uses of RBF networks in a Bayesian
29 context have fixed basis functions with the output layer as the only
30 adjustable parameters. In particular, the Neuroscale output error function
31 is designed to work only with this mask.
32
33 <p>The return value
34 <CODE>prior</CODE> is a data structure,
35 with fields <CODE>prior.alpha</CODE> and <CODE>prior.index</CODE>, which
36 specifies a Gaussian prior distribution for the network weights in an
37 RBF network. The parameters <CODE>aw2</CODE> and <CODE>ab2</CODE> are all
38 scalars and represent the regularization coefficients for two groups
39 of parameters in the network corresponding to
40 second-layer weights, and second-layer biases
41 respectively. Then <CODE>prior.alpha</CODE> represents a column vector of
42 length 2 containing the parameters, and <CODE>prior.index</CODE> is a matrix
43 specifying which weights belong in each group. Each column has one
44 element for each weight in the matrix, using the standard ordering as
45 defined in <CODE>rbfpak</CODE>, and each element is 1 or 0 according to
46 whether the weight is a member of the corresponding group or not.
47
48 <p><h2>
49 See Also
50 </h2>
51 <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE><hr>
52 <b>Pages:</b>
53 <a href="index.htm">Index</a>
54 <hr>
55 <p>Copyright (c) Ian T Nabney (1996-9)
56
57
58 </body>
59 </html>