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Trainable mapping for estimating Parzen densities

    W = PARZENM(A,H)
    W = A*PARZENM([],H)
    W = A*PARZENM(H)

    D = B*W

 A Input dataset
 H Smoothing parameters (scalar, vector)

 W output mapping


A Parzen distribution is estimated for the labeled objects in A. Unlabeled  objects are neglected, unless A is entirely unlabeled or double. Then all  objects are used. If A is a multi-class dataset the densities are estimated  class by class and then weighted and combined according to their prior  probabilities. In all cases, just a single density estimator W is computed.

The mapping W may be applied to a new dataset B using DENSITY = B*W.

The smoothing parameter H is estimated by PARZENML if not supplied. It  can be a scalar or a vector with as many components as A has features.  Note that PARZENML my may be stopped prematurely by PRTIME.


prex_density, for, densities, and, prex_parzen, for, differences, between,


See also

datasets, mappings, knnm, gaussm, parzenml, parzendc, parzenc, knnm, prex_parzen, prtime,

PRTools Contents

PRTools User Guide

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