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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual gbayes
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> gbayes
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Evaluate gradient of Bayesian error function for network.
wolffd@0 14
wolffd@0 15 <p><h2>
wolffd@0 16 Synopsis
wolffd@0 17 </h2>
wolffd@0 18 <PRE>
wolffd@0 19 g = gbayes(net, gdata)
wolffd@0 20 [g, gdata, gprior] = gbayes(net, gdata)
wolffd@0 21 </PRE>
wolffd@0 22
wolffd@0 23
wolffd@0 24 <p><h2>
wolffd@0 25 Description
wolffd@0 26 </h2>
wolffd@0 27 <CODE>g = gbayes(net, gdata)</CODE> takes a network data structure <CODE>net</CODE> together
wolffd@0 28 the data contribution to the error gradient
wolffd@0 29 for a set of inputs and targets.
wolffd@0 30 It returns the regularised error gradient using any zero mean Gaussian priors
wolffd@0 31 on the weights defined in
wolffd@0 32 <CODE>net</CODE>. In addition, if a <CODE>mask</CODE> is defined in <CODE>net</CODE>, then
wolffd@0 33 the entries in <CODE>g</CODE> that correspond to weights with a 0 in the
wolffd@0 34 mask are removed.
wolffd@0 35
wolffd@0 36 <p><CODE>[g, gdata, gprior] = gbayes(net, gdata)</CODE> additionally returns the
wolffd@0 37 data and prior components of the error.
wolffd@0 38
wolffd@0 39 <p><h2>
wolffd@0 40 See Also
wolffd@0 41 </h2>
wolffd@0 42 <CODE><a href="errbayes.htm">errbayes</a></CODE>, <CODE><a href="glmgrad.htm">glmgrad</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE><hr>
wolffd@0 43 <b>Pages:</b>
wolffd@0 44 <a href="index.htm">Index</a>
wolffd@0 45 <hr>
wolffd@0 46 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 47
wolffd@0 48
wolffd@0 49 </body>
wolffd@0 50 </html>