Thesis (MSc)--Stellenbosch University, 2004. / ENGLISH ABSTRACT: It is possible to improve on the accuracy of a single neural network by using
an ensemble of diverse and accurate networks. This thesis explores diversity
in ensembles and looks at the underlying theory and mechanisms employed
to generate and combine ensemble members. Bagging and boosting are
studied in detail and I explain their success in terms of well-known theoretical
instruments. An empirical evaluation of their performance is conducted
and I compare them to a single classifier and to each other in terms of accuracy
and diversity. / AFRIKAANSE OPSOMMING: Dit is moontlik om op die akkuraatheid van 'n enkele neurale netwerk te verbeter
deur 'n ensemble van diverse en akkurate netwerke te gebruik. Hierdie
tesis ondersoek diversiteit in ensembles, asook die meganismes waardeur
lede van 'n ensemble geskep en gekombineer kan word. Die algoritmes
"bagging" en "boosting" word in diepte bestudeer en hulle sukses word aan
die hand van bekende teoretiese instrumente verduidelik. Die prestasie van
hierdie twee algoritmes word eksperimenteel gemeet en hulle akkuraatheid
en diversiteit word met 'n enkele netwerk vergelyk.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/50035 |
| Date | 04 1900 |
| Creators | De Jongh, Albert |
| Contributors | Cloete, Ian, Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 104 leaves : ill. |
| Rights | Stellenbosch University |
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