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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 demglm2 | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> demglm2 | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Demonstrate simple classification using a generalized linear model. | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 demglm1</PRE> | |
| 20 | |
| 21 | |
| 22 <p><h2> | |
| 23 Description | |
| 24 </h2> | |
| 25 | |
| 26 The problem consists of a two dimensional input | |
| 27 matrix <CODE>data</CODE> and a vector of classifications <CODE>t</CODE>. The data is | |
| 28 generated from three Gaussian clusters, and a generalized linear model | |
| 29 with softmax output is trained using iterative reweighted least squares. | |
| 30 A plot of the data together with regions shaded by the classification | |
| 31 given by the network is generated. | |
| 32 | |
| 33 <p><h2> | |
| 34 See Also | |
| 35 </h2> | |
| 36 <CODE><a href="demglm1.htm">demglm1</a></CODE>, <CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE><hr> | |
| 37 <b>Pages:</b> | |
| 38 <a href="index.htm">Index</a> | |
| 39 <hr> | |
| 40 <p>Copyright (c) Ian T Nabney (1996-9) | |
| 41 | |
| 42 | |
| 43 </body> | |
| 44 </html> |