WEAKC

Weak Classifier

    [W,V] = WEAKC(A,ALF,N,CLASSF)
    [W,V] = A*WEAKC(ALF,N,CLASSF)
     VC = WEAKC(A,ALF,N,CLASSF,1)

Input
A	Dataset
ALF	Fraction or number of objects to be used for training, see
GENDAT.	Default: one object per class. For integer ALF, ALF objects per class are generated. Default 1;
N	Number of classifiers to be generated, default 1.
CLASSF	untrained classifier, default NMC

Output
W	Best classifier over ITER runs
V	Cell array of all classifiers  Use VC = stacked(V) for combining
VC	Combined set of classifiers

Description

WEAKC uses subsampled versions of A for training. Testing is done  on the entire training set A. The best classifier is returned in W.  VC combines all classifiers as a stacked combiner in VC.

This routine offers several ways to construct a wea classifier. The  larger ALF, the larger ITER or the more complex CLASSF, the less weak the  resulting classifier W will be. The combined classifier VC is for large  values of N not a weak classifier.

For multi-class problem results may be improved in some cases, e.g.  for small N and / or simple CLASSF, by W = A*(WEKAC*QDC([],[],1e-6))

See also

mappings, datasets, nmc, gendat, stacked,