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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual rbferr
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> rbferr
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Evaluate error function for RBF network.
wolffd@0 14
wolffd@0 15 <p><h2>
wolffd@0 16 Synopsis
wolffd@0 17 </h2>
wolffd@0 18 <PRE>
wolffd@0 19 e = rbferr(net, x, t)
wolffd@0 20 [e, edata, eprior] = rbferr(net, x, t)
wolffd@0 21 </PRE>
wolffd@0 22
wolffd@0 23
wolffd@0 24 <p><h2>
wolffd@0 25 Description
wolffd@0 26 </h2>
wolffd@0 27 <CODE>e = rbferr(net, x, t)</CODE> takes a network data structure <CODE>net</CODE> together
wolffd@0 28 with a matrix <CODE>x</CODE> of input
wolffd@0 29 vectors and a matrix <CODE>t</CODE> of target vectors, and evaluates the
wolffd@0 30 appropriate error function <CODE>e</CODE> depending on <CODE>net.outfn</CODE>.
wolffd@0 31 Each row of <CODE>x</CODE> corresponds to one
wolffd@0 32 input vector and each row of <CODE>t</CODE> contains the corresponding target vector.
wolffd@0 33
wolffd@0 34 <p><CODE>[e, edata, eprior] = rbferr(net, x, t)</CODE> additionally returns the
wolffd@0 35 data and prior components of the error, assuming a zero mean Gaussian
wolffd@0 36 prior on the weights with inverse variance parameters <CODE>alpha</CODE> and
wolffd@0 37 <CODE>beta</CODE> taken from the network data structure <CODE>net</CODE>.
wolffd@0 38
wolffd@0 39 <p><h2>
wolffd@0 40 See Also
wolffd@0 41 </h2>
wolffd@0 42 <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbftrain.htm">rbftrain</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE><hr>
wolffd@0 43 <b>Pages:</b>
wolffd@0 44 <a href="index.htm">Index</a>
wolffd@0 45 <hr>
wolffd@0 46 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 47
wolffd@0 48
wolffd@0 49 </body>
wolffd@0 50 </html>