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| author | Daniel Wolff |
|---|---|
| date | Fri, 19 Aug 2016 13:07:06 +0200 |
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| children |
| rev | line source |
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| Daniel@0 | 1 <html> |
| Daniel@0 | 2 <head> |
| Daniel@0 | 3 <title> |
| Daniel@0 | 4 Netlab Reference Manual demglm2 |
| Daniel@0 | 5 </title> |
| Daniel@0 | 6 </head> |
| Daniel@0 | 7 <body> |
| Daniel@0 | 8 <H1> demglm2 |
| Daniel@0 | 9 </H1> |
| Daniel@0 | 10 <h2> |
| Daniel@0 | 11 Purpose |
| Daniel@0 | 12 </h2> |
| Daniel@0 | 13 Demonstrate simple classification using a generalized linear model. |
| Daniel@0 | 14 |
| Daniel@0 | 15 <p><h2> |
| Daniel@0 | 16 Synopsis |
| Daniel@0 | 17 </h2> |
| Daniel@0 | 18 <PRE> |
| Daniel@0 | 19 demglm1</PRE> |
| Daniel@0 | 20 |
| Daniel@0 | 21 |
| Daniel@0 | 22 <p><h2> |
| Daniel@0 | 23 Description |
| Daniel@0 | 24 </h2> |
| Daniel@0 | 25 |
| Daniel@0 | 26 The problem consists of a two dimensional input |
| Daniel@0 | 27 matrix <CODE>data</CODE> and a vector of classifications <CODE>t</CODE>. The data is |
| Daniel@0 | 28 generated from three Gaussian clusters, and a generalized linear model |
| Daniel@0 | 29 with softmax output is trained using iterative reweighted least squares. |
| Daniel@0 | 30 A plot of the data together with regions shaded by the classification |
| Daniel@0 | 31 given by the network is generated. |
| Daniel@0 | 32 |
| Daniel@0 | 33 <p><h2> |
| Daniel@0 | 34 See Also |
| Daniel@0 | 35 </h2> |
| Daniel@0 | 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> |
| Daniel@0 | 37 <b>Pages:</b> |
| Daniel@0 | 38 <a href="index.htm">Index</a> |
| Daniel@0 | 39 <hr> |
| Daniel@0 | 40 <p>Copyright (c) Ian T Nabney (1996-9) |
| Daniel@0 | 41 |
| Daniel@0 | 42 |
| Daniel@0 | 43 </body> |
| Daniel@0 | 44 </html> |