wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual knn wolffd@0: wolffd@0: wolffd@0: wolffd@0:

knn wolffd@0:

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wolffd@0: Purpose wolffd@0:

wolffd@0: Creates a K-nearest-neighbour classifier. wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

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wolffd@0: 
wolffd@0: net = knn(nin, nout, k, tr_in, tr_targets)
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wolffd@0: Description wolffd@0:

wolffd@0: net = knn(nin, nout, k, tr_in, tr_targets) creates a KNN model net wolffd@0: with input dimension nin, output dimension nout and k wolffd@0: neighbours. The training data is also stored in the data structure and the wolffd@0: targets are assumed to be using a 1-of-N coding. wolffd@0: wolffd@0:

The fields in net are wolffd@0:

wolffd@0: 
wolffd@0:   type = 'knn'
wolffd@0:   nin = number of inputs
wolffd@0:   nout = number of outputs
wolffd@0:   tr_in = training input data
wolffd@0:   tr_targets = training target data
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wolffd@0: See Also wolffd@0:

wolffd@0: kmeans, knnfwd

wolffd@0: Pages: wolffd@0: Index wolffd@0:

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