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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
|---|---|---|
| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/knn.htm | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz | |
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02
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diff --git a/sourcecodes/bnt-master/nethelp3.3/knn.htm b/sourcecodes/bnt-master/nethelp3.3/knn.htm new file mode 100644 index 00000000..31f032bc --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/knn.htm @@ -0,0 +1,54 @@ +<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 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) + + +</body> +</html> \ No newline at end of file |