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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 glmerr
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5 </title>
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6 </head>
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7 <body>
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8 <H1> glmerr
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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 error function for generalized linear model.
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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 e = glmerr(net, x, t)
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20 [e, edata, eprior] = glmerr(net, x, t)
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21 [e, edata, eprior, y, a] = glmerr(net, x, t)
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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
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29 <CODE>e = glmerr(net, x, t)</CODE> takes a generalized
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30 linear model data structure <CODE>net</CODE> together with a matrix <CODE>x</CODE>
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31 of input vectors and a matrix <CODE>t</CODE> of target vectors, and evaluates
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32 the error function <CODE>e</CODE>. The choice of error function corresponds
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33 to the output unit activation function. Each row of <CODE>x</CODE>
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34 corresponds to one input vector and each row of <CODE>t</CODE> corresponds to
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35 one target vector.
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36
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37 <p><CODE>[e, edata, eprior, y, a] = glmerr(net, x, t)</CODE> also returns
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38 the data and prior components of the total error.
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39
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40 <p><CODE>[e, edata, eprior, y, a] = glmerr(net, x)</CODE> also returns a matrix <CODE>y</CODE>
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41 giving the outputs of the models and a matrix <CODE>a</CODE>
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42 giving the summed inputs to each output unit, where each row
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43 corresponds to one pattern.
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44
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45 <p><h2>
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46 See Also
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47 </h2>
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48 <CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="glmfwd.htm">glmfwd</a></CODE>, <CODE><a href="glmgrad.htm">glmgrad</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE><hr>
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49 <b>Pages:</b>
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50 <a href="index.htm">Index</a>
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51 <hr>
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52 <p>Copyright (c) Ian T Nabney (1996-9)
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53
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54
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55 </body>
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56 </html> |
