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<html><head><title>Netlab Reference Manual knn</title></head><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 thetargets 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)</body></html>?? 快捷鍵說明
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