CROSSVALD

Cross-validation error for dissimilarity representations

    [ERR,STD_ERR] = CROSSVALD(D,CLASSF,N,K,ITER)
    [ERR,CERR,NLAB_OUT] = CROSSVALD(D,CLASSF,N,K,1)

Input
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

Output
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

Description

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.

Example(s)

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

See also

datasets, mappings, testc,