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Cross-validation error for dissimilarity representations


 A Input dataset
 CLASSF Untrained classifier to be tested.
 N Number of dataset divisions (default: N==number of  samples, leave-one-out)
 K Desired size of the representation set (default: use all)
 ITER Number of iterations

 ERR Average test error weighted by class priors.
 CERR Unweighted test errors per class
 STD_ERR Standard deviation in the error
 NLAB_OUT Assigned numeric labels


Cross-validation estimation of the error and the instability of the  untrained classifier CLASSF using the dissimilarity dataset D. The set is  randomly permutated and divided in N (almost) equally sized parts. Note that  for a dissimilarity matrix, the division has to be applied both to rows and  to columns. The classifier is trained on N-1 parts and the remaining part is  used for testing. This is rotated over all parts.

ERR is their weighted avarage over the class priors. CERR are the class error

frequencies. D and/or CLASSF may be cell arrays of datasets and classifiers.
In that case ERR is an array with errors with on position ERR(i,j) the error of
the j-th classifier for the i-th dataset. In this mode, CERR and NLAB_OUT are
returned in cell arrays.

If ITER > 1 the routine is run ITER times and results are averaged. The  standard deviation of the error is returned in STD_ERR.

NOTE D is a square dissimilarity matrix for which the representation set has to be  reduced to a K-element subset from the training set. This is done by random  selection. If K is not chosen the entire training set is used.


 A = GENDATB(100);
 [E,S] = CROSSVALD (D, ldc([],1e-2,1e-6)*LOGDENS, 10, [], 5);

See also

datasets, mappings, testc,

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DisTools User Guide

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