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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 fevbayes
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5 </title>
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6 </head>
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7 <body>
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8 <H1> fevbayes
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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 Evaluate Bayesian regularisation for network forward propagation.
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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 extra = fevbayes(net, y, a, x, t, x_test)
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20 [extra, invhess] = fevbayes(net, y, a, x, t, x_test, invhess)
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21 </PRE>
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22
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23
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24 <p><h2>
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25 Description
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26 </h2>
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27 <CODE>extra = fevbayes(net, y, a, x, t, x_test)</CODE> takes a network data structure
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28 <CODE>net</CODE> together with a set of hidden unit activations <CODE>a</CODE> from
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29 test inputs <CODE>x_test</CODE>, training data inputs <CODE>x</CODE> and <CODE>t</CODE> and
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30 outputs a matrix of extra information <CODE>extra</CODE> that consists of
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31 error bars (variance)
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32 for a regression problem or moderated outputs for a classification problem.
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33 The optional argument (and return value)
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34 <CODE>invhess</CODE> is the inverse of the network Hessian
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35 computed on the training data inputs and targets. Passing it in avoids
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36 recomputing it, which can be a significant saving for large training sets.
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37
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38 <p>This is called by network-specific functions such as <CODE>mlpevfwd</CODE> which
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39 are needed since the return values (predictions and hidden unit activations)
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40 for different network types are in different orders (for good reasons).
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41
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42 <p><h2>
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43 See Also
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44 </h2>
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45 <CODE><a href="mlpevfwd.htm">mlpevfwd</a></CODE>, <CODE><a href="rbfevfwd.htm">rbfevfwd</a></CODE>, <CODE><a href="glmevfwd.htm">glmevfwd</a></CODE><hr>
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46 <b>Pages:</b>
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47 <a href="index.htm">Index</a>
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48 <hr>
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49 <p>Copyright (c) Ian T Nabney (1996-9)
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50
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51
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52 </body>
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53 </html> |
