CLEVALD

Classifier evaluation (learning curve) for dissimilarity data

    E = CLEVALD(D,CLASSF,TRAINSIZES,REPSIZE,NREPS,T,TESTFUN)

Input
D	Square dissimilarity dataset
CLASSF	Classifiers to be evaluated (cell array)
TRAINSIZE	Vector of training set sizes, used to generate subsets of D (default [2,3,5,7,10,15,20,30,50,70,100]). TRAINSIZE is per  class unless D has no priors set or has soft labels.
REPSIZE	Representation set size per class (>=1), or fraction (<1) (default total training set). If REPSIZE > TRAINSIZE testobjects are included for representation.
NREPS	Number of repetitions (default 1)
T	Test dataset (default [], use remaining samples in A)
TESTFUN	Mapping,evaluation function (default classification error)

Output
E	Error structure (see PLOTE) containing training and test  errors

Description

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.

See also

mappings, datasets, cleval, testc, plote,