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<title>
Netlab Reference Manual knn
</title>
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<body>
<H1> knn
</H1>
<h2>
Purpose
</h2>
Creates a K-nearest-neighbour classifier.
<p><h2>
Synopsis
</h2>
<PRE>
net = knn(nin, nout, k, tr_in, tr_targets)
</PRE>
<p><h2>
Description
</h2>
<CODE>net = knn(nin, nout, k, tr_in, tr_targets)</CODE> creates a KNN model <CODE>net</CODE>
with input dimension <CODE>nin</CODE>, output dimension <CODE>nout</CODE> and <CODE>k</CODE>
neighbours. The training data is also stored in the data structure and the
targets are assumed to be using a 1-of-N coding.
<p>The fields in <CODE>net</CODE> are
<PRE>
type = 'knn'
nin = number of inputs
nout = number of outputs
tr_in = training input data
tr_targets = training target data
</PRE>
<p><h2>
See Also
</h2>
<CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="knnfwd.htm">knnfwd</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
<hr>
<p>Copyright (c) Ian T Nabney (1996-9)
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