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| author | wolffd |
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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 gbayes | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> gbayes | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Evaluate gradient of Bayesian error function for network. | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 g = gbayes(net, gdata) | |
| 20 [g, gdata, gprior] = gbayes(net, gdata) | |
| 21 </PRE> | |
| 22 | |
| 23 | |
| 24 <p><h2> | |
| 25 Description | |
| 26 </h2> | |
| 27 <CODE>g = gbayes(net, gdata)</CODE> takes a network data structure <CODE>net</CODE> together | |
| 28 the data contribution to the error gradient | |
| 29 for a set of inputs and targets. | |
| 30 It returns the regularised error gradient using any zero mean Gaussian priors | |
| 31 on the weights defined in | |
| 32 <CODE>net</CODE>. In addition, if a <CODE>mask</CODE> is defined in <CODE>net</CODE>, then | |
| 33 the entries in <CODE>g</CODE> that correspond to weights with a 0 in the | |
| 34 mask are removed. | |
| 35 | |
| 36 <p><CODE>[g, gdata, gprior] = gbayes(net, gdata)</CODE> additionally returns the | |
| 37 data and prior components of the error. | |
| 38 | |
| 39 <p><h2> | |
| 40 See Also | |
| 41 </h2> | |
| 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> | |
| 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> |