annotate toolboxes/FullBNT-1.0.7/nethelp3.3/glmevfwd.htm @ 0:e9a9cd732c1e
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first hg version after svn
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Tue, 10 Feb 2015 15:05:51 +0000 (2015-02-10) |
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1 <html>
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2 <head>
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3 <title>
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4 Netlab Reference Manual glmevfwd
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5 </title>
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6 </head>
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7 <body>
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8 <H1> glmevfwd
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9 </H1>
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10 <h2>
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11 Purpose
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12 </h2>
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13 Forward propagation with evidence for GLM
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14
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15 <p><h2>
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16 Synopsis
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17 </h2>
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18 <PRE>
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19
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20 [y, extra] = glmevfwd(net, x, t, x_test)
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21 [y, extra, invhess] = glmevfwd(net, x, t, x_test, invhess)
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22 </PRE>
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23
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24
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25 <p><h2>
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26 Description
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27 </h2>
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28 <CODE>y = glmevfwd(net, x, t, x_test)</CODE> takes a network data structure
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29 <CODE>net</CODE> together with the input <CODE>x</CODE> and target <CODE>t</CODE> training data
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30 and input test data <CODE>x_test</CODE>.
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31 It returns the normal forward propagation through the network <CODE>y</CODE>
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32 together with a matrix <CODE>extra</CODE> which consists of error bars (variance)
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33 for a regression problem or moderated outputs for a classification problem.
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34
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35 <p>The optional argument (and return value)
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36 <CODE>invhess</CODE> is the inverse of the network Hessian
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37 computed on the training data inputs and targets. Passing it in avoids
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38 recomputing it, which can be a significant saving for large training sets.
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39
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40 <p><h2>
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41 See Also
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42 </h2>
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43 <CODE><a href="fevbayes.htm">fevbayes</a></CODE><hr>
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44 <b>Pages:</b>
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45 <a href="index.htm">Index</a>
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46 <hr>
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47 <p>Copyright (c) Ian T Nabney (1996-9)
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48
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49
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50 </body>
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51 </html> |