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1 <html> | |
2 <head> | |
3 <title> | |
4 Netlab Reference Manual rbfhess | |
5 </title> | |
6 </head> | |
7 <body> | |
8 <H1> rbfhess | |
9 </H1> | |
10 <h2> | |
11 Purpose | |
12 </h2> | |
13 Evaluate the Hessian matrix for RBF network. | |
14 | |
15 <p><h2> | |
16 Synopsis | |
17 </h2> | |
18 <PRE> | |
19 h = rbfhess(net, x, t) | |
20 [h, hdata] = rbfhess(net, x, t) | |
21 h = rbfhess(net, x, t, hdata) | |
22 </PRE> | |
23 | |
24 | |
25 <p><h2> | |
26 Description | |
27 </h2> | |
28 <CODE>h = rbfhess(net, x, t)</CODE> takes an RBF network data structure <CODE>net</CODE>, | |
29 a matrix <CODE>x</CODE> of input values, and a matrix <CODE>t</CODE> of target | |
30 values and returns the full Hessian matrix <CODE>h</CODE> corresponding to | |
31 the second derivatives of the negative log posterior distribution, | |
32 evaluated for the current weight and bias values as defined by | |
33 <CODE>net</CODE>. Currently, the implementation only computes the | |
34 Hessian for the output layer weights. | |
35 | |
36 <p><CODE>[h, hdata] = rbfhess(net, x, t)</CODE> returns both the Hessian matrix | |
37 <CODE>h</CODE> and the contribution <CODE>hdata</CODE> arising from the data dependent | |
38 term in the Hessian. | |
39 | |
40 <p><CODE>h = rbfhess(net, x, t, hdata)</CODE> takes a network data structure | |
41 <CODE>net</CODE>, a matrix <CODE>x</CODE> of input values, and a matrix <CODE>t</CODE> of | |
42 target values, together with the contribution <CODE>hdata</CODE> arising from | |
43 the data dependent term in the Hessian, and returns the full Hessian | |
44 matrix <CODE>h</CODE> corresponding to the second derivatives of the negative | |
45 log posterior distribution. This version saves computation time if | |
46 <CODE>hdata</CODE> has already been evaluated for the current weight and bias | |
47 values. | |
48 | |
49 <p><h2> | |
50 Example | |
51 </h2> | |
52 For the standard regression framework with a Gaussian conditional | |
53 distribution of target values given input values, and a simple | |
54 Gaussian prior over weights, the Hessian takes the form | |
55 <PRE> | |
56 | |
57 h = beta*hdata + alpha*I | |
58 </PRE> | |
59 | |
60 | |
61 <p><h2> | |
62 See Also | |
63 </h2> | |
64 <CODE><a href="mlphess.htm">mlphess</a></CODE>, <CODE><a href="hesschek.htm">hesschek</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE><hr> | |
65 <b>Pages:</b> | |
66 <a href="index.htm">Index</a> | |
67 <hr> | |
68 <p>Copyright (c) Ian T Nabney (1996-9) | |
69 | |
70 | |
71 </body> | |
72 </html> |