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CLEVALS
E = CLEVALS(A,CLASSF,FEATSIZE,TRAINSIZES,NREPS,T)
| Input | |
| A | Training dataset |
| CLASSF | Classifier to evaluate |
| FEATSIZE | Vector of feature sizes (default: 1:K, where K is the number of features in A) |
| TRAINSIZES | Vector of class sizes, used to generate subsets of A (default [2,3,5,7,10,15,20,30,50,70,100]) |
| NREPS | Number of repetitions (default 1) |
| T | Tuning set, or 'bootstrap' (default [], i.e. use remaining objects in A) |
| Output | |
| E | Error structure (see PLOTE) |
Generates at random, for all feature sizes defined in FEATSIZES or all class sizes defined in TRAINSIZES, training sets out of the dataset A and uses these for training the untrained classifier CLASSF. CLASSF may also be a cell array of untrained classifiers; in this case the routine will be run for all of them. The resulting trained classifiers are tested on all objects in A. This procedure is then repeated N times.
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.
If CLASSF is fully deterministic, this function uses the RAND random generator and thereby reproduces if its seed is reset (see RAND). If CLASSF uses RANDN, its seed may have to be set as well.
prex_cleval,
mappings, datasets, clevalb, testc, plote,
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