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| author | Daniel Wolff |
|---|---|
| date | Fri, 19 Aug 2016 13:07:06 +0200 |
| parents | |
| 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 demmlp2 |
| Daniel@0 | 5 </title> |
| Daniel@0 | 6 </head> |
| Daniel@0 | 7 <body> |
| Daniel@0 | 8 <H1> demmlp2 |
| Daniel@0 | 9 </H1> |
| Daniel@0 | 10 <h2> |
| Daniel@0 | 11 Purpose |
| Daniel@0 | 12 </h2> |
| Daniel@0 | 13 Demonstrate simple classification using a multi-layer perceptron |
| Daniel@0 | 14 |
| Daniel@0 | 15 <p><h2> |
| Daniel@0 | 16 Synopsis |
| Daniel@0 | 17 </h2> |
| Daniel@0 | 18 <PRE> |
| Daniel@0 | 19 demmlp2</PRE> |
| Daniel@0 | 20 |
| Daniel@0 | 21 |
| Daniel@0 | 22 <p><h2> |
| Daniel@0 | 23 Description |
| Daniel@0 | 24 </h2> |
| Daniel@0 | 25 The problem consists of input data in two dimensions drawn from a mixture |
| Daniel@0 | 26 of three Gaussians: two of which are assigned to a single class. An MLP |
| Daniel@0 | 27 with logistic outputs trained with a quasi-Newton optimisation algorithm is |
| Daniel@0 | 28 compared with the optimal Bayesian decision rule. |
| Daniel@0 | 29 |
| Daniel@0 | 30 <p><h2> |
| Daniel@0 | 31 See Also |
| Daniel@0 | 32 </h2> |
| Daniel@0 | 33 <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlpfwd.htm">mlpfwd</a></CODE>, <CODE><a href="neterr.htm">neterr</a></CODE>, <CODE><a href="quasinew.htm">quasinew</a></CODE><hr> |
| Daniel@0 | 34 <b>Pages:</b> |
| Daniel@0 | 35 <a href="index.htm">Index</a> |
| Daniel@0 | 36 <hr> |
| Daniel@0 | 37 <p>Copyright (c) Ian T Nabney (1996-9) |
| Daniel@0 | 38 |
| Daniel@0 | 39 |
| Daniel@0 | 40 </body> |
| Daniel@0 | 41 </html> |