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comparison toolboxes/FullBNT-1.0.7/nethelp3.3/knn.htm @ 0:e9a9cd732c1e tip
first hg version after svn
| 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 knn | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> knn | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Creates a K-nearest-neighbour classifier. | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 | |
| 20 net = knn(nin, nout, k, tr_in, tr_targets) | |
| 21 </PRE> | |
| 22 | |
| 23 | |
| 24 <p><h2> | |
| 25 Description | |
| 26 </h2> | |
| 27 <CODE>net = knn(nin, nout, k, tr_in, tr_targets)</CODE> creates a KNN model <CODE>net</CODE> | |
| 28 with input dimension <CODE>nin</CODE>, output dimension <CODE>nout</CODE> and <CODE>k</CODE> | |
| 29 neighbours. The training data is also stored in the data structure and the | |
| 30 targets are assumed to be using a 1-of-N coding. | |
| 31 | |
| 32 <p>The fields in <CODE>net</CODE> are | |
| 33 <PRE> | |
| 34 | |
| 35 type = 'knn' | |
| 36 nin = number of inputs | |
| 37 nout = number of outputs | |
| 38 tr_in = training input data | |
| 39 tr_targets = training target data | |
| 40 </PRE> | |
| 41 | |
| 42 | |
| 43 <p><h2> | |
| 44 See Also | |
| 45 </h2> | |
| 46 <CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="knnfwd.htm">knnfwd</a></CODE><hr> | |
| 47 <b>Pages:</b> | |
| 48 <a href="index.htm">Index</a> | |
| 49 <hr> | |
| 50 <p>Copyright (c) Ian T Nabney (1996-9) | |
| 51 | |
| 52 | |
| 53 </body> | |
| 54 </html> |