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Test k-NN classifier for dissimilarity data

    [E,C] = TESTKD(D,K,PAR)
    [E,C] = D*TESTKD([],K,PAR)

 D Dissimilarity dataset. Object labels are assumed to be the  true labels. Feature labels are assumed to be the labels of  the objects they are related to.
 K Desired number of neighbors to take into account, default K = 1.
 PAR 'LOO' - leave-one-out option. This should be used if  the objects are related to themselves. If D is not square,  it is assumed that the first sets of objects in columns and  rows match.
 'ALL' use all objects (default).

 E Estimated error
 C Dataset with confidences, size M x N, if D has size M x L and  the labels are given for N classes. Note that for K < 3 these  confidences are derived from the nearest neigbor distances  and that for K >= 3 they are the Bayes estimators of the  neighborhood class probabilities. D*TESTC returns E.


TESTKD is based on just counting errors and does not weight with class  class priors stored in D. Use D*(DL*KNNDC)*TESTC if this is needed.  DL is the dissimilarity matrix of the representation objects used for D.

See also

datasets, knndc, nne, nnerror1, nnerror2,

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DisTools User Guide

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