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MCLASSM
W = MCLASSM(A,MAPPING,MODE,PAR)
Input | |
A | Dataset |
MAPPING | Untrained mapping |
MODE | Combining mode (optional; default: 'weight') |
PAR | Parameter needed for the combining |
Output | |
W | Combined mapping |
If A is a unlabeled dataset or double matrix, it is converted to a one-class dataset. For one-class datasets A, the mapping is computed, calling the untrained MAPPING using the labeled samples of A only. For multi-class datasets separate mappings are determined for each class in A. They are combined as defined by MODE and PAR. The following combining rules are supported
'weight': weight the mapping outcome for class j by PAR(j) and sum over the classes. This is useful for densities in which case PAR is typically the set of class priors (these are in fact the defaults if MODE = 'weight').
'mean' | combine by averaging. |
'min' | combine by the minimum rule. |
'max' | combine by the maximum rule. |
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