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author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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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> |