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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 gperr | |
5 </title> | |
6 </head> | |
7 <body> | |
8 <H1> gperr | |
9 </H1> | |
10 <h2> | |
11 Purpose | |
12 </h2> | |
13 Evaluate error function for Gaussian Process. | |
14 | |
15 <p><h2> | |
16 Synopsis | |
17 </h2> | |
18 <PRE> | |
19 edata = gperr(net, x, t) | |
20 [e, edata, eprior] = gperr(net, x, t) | |
21 </PRE> | |
22 | |
23 | |
24 <p><h2> | |
25 Description | |
26 </h2> | |
27 <CODE>e = gperr(net, x, t)</CODE> takes a Gaussian Process data structure <CODE>net</CODE> together | |
28 with a matrix <CODE>x</CODE> of input vectors and a matrix <CODE>t</CODE> of target | |
29 vectors, and evaluates the error function <CODE>e</CODE>. Each row | |
30 of <CODE>x</CODE> corresponds to one input vector and each row of <CODE>t</CODE> | |
31 corresponds to one target vector. | |
32 | |
33 <p><CODE>[e, edata, eprior] = gperr(net, x, t)</CODE> additionally returns the | |
34 data and hyperprior components of the error, assuming a Gaussian | |
35 prior on the weights with mean and variance parameters <CODE>prmean</CODE> and | |
36 <CODE>prvariance</CODE> taken from the network data structure <CODE>net</CODE>. | |
37 | |
38 <p><h2> | |
39 See Also | |
40 </h2> | |
41 <CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gpcovar.htm">gpcovar</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE><hr> | |
42 <b>Pages:</b> | |
43 <a href="index.htm">Index</a> | |
44 <hr> | |
45 <p>Copyright (c) Ian T Nabney (1996-9) | |
46 | |
47 | |
48 </body> | |
49 </html> |