Classifier evaluation (learning curve) for dissimilarity data
E = CLEVALD(D,CLASSF,TRAINSIZES,REPSIZE,NREPS,T,TESTFUN)
Generates at random, for all class sizes defined in TRAINSIZES, training sets out of the dissimilarity dataset D. The representation set is either equal to the training set (REPSIZE = ), or a fraction of it (REPSIZE <1) or a random subset of it of a given size (REPSIZE>1). This set is used for training the untrained classifiers CLASSF. The resulting trained classifiers are tested on the training objects and on the left-over test objects, or, if supplied, the testset T. This procedure is then repeated NREPS times. The default test routine is classification error estimation by TESTC(,'crisp').
The returned structure E contains several fields for annotating the plot produced by PLOTE. They may be changed by the users. Removal of the field 'apperror' (RMFIELD(E,'apperror')) suppresses the draw of the error curves for the training set.
Training set generation is done "with replacement" and such that for each run the larger training sets include the smaller ones and that for all classifiers the same training sets are used.
This function uses the RAND random generator and thereby reproduces if its seed is reset (see RAND). If CLASSF uses RANDN, its seed should be reset as well.