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| author | wolffd |
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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 demgpard | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> demgpard | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Demonstrate ARD using a Gaussian Process. | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 demgpare</PRE> | |
| 20 | |
| 21 | |
| 22 <p><h2> | |
| 23 Description | |
| 24 </h2> | |
| 25 The data consists of three input variables <CODE>x1</CODE>, <CODE>x2</CODE> and | |
| 26 <CODE>x3</CODE>, and one target variable | |
| 27 <CODE>t</CODE>. The | |
| 28 target data is generated by computing <CODE>sin(2*pi*x1)</CODE> and adding Gaussian | |
| 29 noise, x2 is a copy of x1 with a higher level of added | |
| 30 noise, and x3 is sampled randomly from a Gaussian distribution. | |
| 31 A Gaussian Process, is | |
| 32 trained by optimising the hyperparameters | |
| 33 using the scaled conjugate gradient algorithm. The final values of the | |
| 34 hyperparameters show that the model successfully identifies the importance | |
| 35 of each input. | |
| 36 | |
| 37 <p><h2> | |
| 38 See Also | |
| 39 </h2> | |
| 40 <CODE><a href="demgp.htm">demgp</a></CODE>, <CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE>, <CODE><a href="gpinit.htm">gpinit</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> | |
| 41 <b>Pages:</b> | |
| 42 <a href="index.htm">Index</a> | |
| 43 <hr> | |
| 44 <p>Copyright (c) Ian T Nabney (1996-9) | |
| 45 | |
| 46 | |
| 47 </body> | |
| 48 </html> |