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comparison toolboxes/FullBNT-1.0.7/nethelp3.3/glmevfwd.htm @ 0:e9a9cd732c1e tip
first hg version after svn
| 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 glmevfwd | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> glmevfwd | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Forward propagation with evidence for GLM | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 | |
| 20 [y, extra] = glmevfwd(net, x, t, x_test) | |
| 21 [y, extra, invhess] = glmevfwd(net, x, t, x_test, invhess) | |
| 22 </PRE> | |
| 23 | |
| 24 | |
| 25 <p><h2> | |
| 26 Description | |
| 27 </h2> | |
| 28 <CODE>y = glmevfwd(net, x, t, x_test)</CODE> takes a network data structure | |
| 29 <CODE>net</CODE> together with the input <CODE>x</CODE> and target <CODE>t</CODE> training data | |
| 30 and input test data <CODE>x_test</CODE>. | |
| 31 It returns the normal forward propagation through the network <CODE>y</CODE> | |
| 32 together with a matrix <CODE>extra</CODE> which consists of error bars (variance) | |
| 33 for a regression problem or moderated outputs for a classification problem. | |
| 34 | |
| 35 <p>The optional argument (and return value) | |
| 36 <CODE>invhess</CODE> is the inverse of the network Hessian | |
| 37 computed on the training data inputs and targets. Passing it in avoids | |
| 38 recomputing it, which can be a significant saving for large training sets. | |
| 39 | |
| 40 <p><h2> | |
| 41 See Also | |
| 42 </h2> | |
| 43 <CODE><a href="fevbayes.htm">fevbayes</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> |