/
glmevfwd.htm
51 lines (43 loc) · 1.18 KB
/
glmevfwd.htm
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
<html>
<head>
<title>
Netlab Reference Manual glmevfwd
</title>
</head>
<body>
<H1> glmevfwd
</H1>
<h2>
Purpose
</h2>
Forward propagation with evidence for GLM
<p><h2>
Synopsis
</h2>
<PRE>
[y, extra] = glmevfwd(net, x, t, x_test)
[y, extra, invhess] = glmevfwd(net, x, t, x_test, invhess)
</PRE>
<p><h2>
Description
</h2>
<CODE>y = glmevfwd(net, x, t, x_test)</CODE> takes a network data structure
<CODE>net</CODE> together with the input <CODE>x</CODE> and target <CODE>t</CODE> training data
and input test data <CODE>x_test</CODE>.
It returns the normal forward propagation through the network <CODE>y</CODE>
together with a matrix <CODE>extra</CODE> which consists of error bars (variance)
for a regression problem or moderated outputs for a classification problem.
<p>The optional argument (and return value)
<CODE>invhess</CODE> is the inverse of the network Hessian
computed on the training data inputs and targets. Passing it in avoids
recomputing it, which can be a significant saving for large training sets.
<p><h2>
See Also
</h2>
<CODE><a href="fevbayes.htm">fevbayes</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
<hr>
<p>Copyright (c) Ian T Nabney (1996-9)
</body>
</html>