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parzenc

PARZENC

Optimisation of the Parzen classifier

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

Input
 A dataset
 H smoothing parameter (may be scalar, vector of per-class  parameters, or matrix with parameters for each class (rows) and  dimension (columns))

Output
 W trained mapping
 H estimated smoothing (scalar value)

Description

Computation of the optimum smoothing parameter H for the Parzen  classifier between the classes in the dataset A. The leave-one-out  Lissack & Fu estimate is used in the optimisation of H The final  classifier is stored as a mapping in W. It may be converted  into a classifier by W*CLASSC. PARZENC cannot be used for density  estimation. The returned value of H, however, can be used in a the Parzen  density estimator PARZENM.

The optimisation of H may be stopped prematurely by PRTIME.

In case smoothing H is specified, no learning is performed, just the  discriminant W is produced for the given smoothing parameters H.  Smoothing parameters may be scalar, vector of per-class parameters, or  a matrix with individual smoothing for each class (rows) and feature  directions (columns)

Example(s)

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

 PARZENC, PARZENDC and PARZENM, PRTIME

Reference(s)

T. Lissack and K.S. Fu, Error estimation in pattern recognition via L-distance between posterior density functions, IEEE Trans. Inform. Theory, vol. 22, pp. 34-45, 1976.

See also

datasets, mappings, parzen_map, parzenml, parzendc, parzenm, classc, prex_parzen,

PRTools Contents

PRTools User Guide

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