139 leaves printed single pages, preliminary pages i-xi and numbered pages 1-127. Includes bibliography and a list of figures and tables. Digitized at 600 dpi grayscale to pdf format (OCR),using a Bizhub 250 Konica Minolta Scanner. / Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2003. / ENGLISH ABSTRACT: Probability density functions (PDFs) and cumulative distribution functions (CDFs)
play a central role in statistical pattern recognition and verification systems. They allow
observations that do not occur according to deterministic rules to be quantified and modelled.
An example of such observations would be the voice patterns of a person that is
used as input to a biometric security device.
In order to model such non-deterministic observations, a density function estimator
is employed to estimate a PDF or CDF from sample data. Although numerous density
function estimation techniques exist, all the techniques can be classified into one of two
groups, parametric and non-parametric, each with its own characteristic advantages and
disadvantages.
In this research, we introduce a novel approach to density function estimation that
attempts to combine some of the advantages of both the parametric and non-parametric
estimators. This is done by considering density estimation using an abstract approach in
which the density function is modelled entirely in terms of its moments or characteristic
function. New density function estimation techniques are first developed in theory, after
which a number of practical density function estimators are presented.
Experiments are performed in which the performance of the new estimators are compared
to two established estimators, namely the Parzen estimator and the Gaussian mixture
model (GMM). The comparison is performed in terms of the accuracy, computational requirements
and ease of use of the estimators and it is found that the new estimators does
combine some of the advantages of the established estimators without the corresponding
disadvantages. / AFRIKAANSE OPSOMMING: Waarskynlikheids digtheidsfunksies (WDFs) en Kumulatiewe distribusiefunksies (KDFs)
speel 'n sentrale rol in statistiese patroonherkenning en verifikasie stelsels. Hulle maak dit
moontlik om nie-deterministiese observasies te kwantifiseer en te modelleer. Die stempatrone
van 'n spreker wat as intree tot 'n biometriese sekuriteits stelsel gegee word, is 'n
voorbeeld van so 'n observasie.
Ten einde sulke observasies te modelleer, word 'n digtheidsfunksie afskatter gebruik om
die WDF of KDF vanaf data monsters af te skat. Alhoewel daar talryke digtheidsfunksie
afskatters bestaan, kan almal in een van twee katagoriee geplaas word, parametries en
nie-parametries, elk met hul eie kenmerkende voordele en nadele.
Hierdie werk Ie 'n nuwe benadering tot digtheidsfunksie afskatting voor wat die voordele
van beide die parametriese sowel as die nie-parametriese tegnieke probeer kombineer. Dit
word gedoen deur digtheidsfunksie afskatting vanuit 'n abstrakte oogpunt te benader waar
die digtheidsfunksie uitsluitlik in terme van sy momente en karakteristieke funksie gemodelleer
word. Nuwe metodes word eers in teorie ondersoek en ontwikkel waarna praktiese
tegnieke voorgele word. Hierdie afskatters het die vermoe om 'n wye verskeidenheid digtheidsfunksies
af te skat en is nie net ontwerp om slegs sekere families van digtheidsfunksies
optimaal voor te stel nie.
Eksperimente is uitgevoer wat die werkverrigting van die nuwe tegnieke met twee gevestigde
tegnieke, naamlik die Parzen afskatter en die Gaussiese mengsel model (GMM), te
vergelyk. Die werkverrigting word gemeet in terme van akkuraatheid, vereiste numeriese
verwerkingsvermoe en die gemak van gebruik. Daar word bevind dat die nuwe afskatters
weI voordele van die gevestigde afskatters kombineer sonder die gepaardgaande nadele.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/1001 |
| Date | 03 1900 |
| Creators | Esterhuizen, Gerhard |
| Contributors | Du Preez, J. A., University of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 127 p. : ill. |
| Rights | University of Stellenbosch |
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