testkd
TESTKD
Test kNN classifier for dissimilarity data
[E,C] = TESTKD(D,K,PAR)
[E,C] = D*TESTKD([],K,PAR)
Input  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'  leaveoneout 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). 
Output  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. 
Description 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, This file has been automatically generated. If badly readable, use the helpcommand in Matlab. 
