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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>