Thesis (MComm)--Stellenbosch University, 2004. / ENGLISH ABSTRACT: Since its introduction in Boser et al. (1992), the support vector machine has become a
popular tool in a variety of machine learning applications. More recently, the support
vector machine has also been receiving increasing attention in the statistical
community as a tool for classification and regression. In this thesis support vector
machines are compared to more traditional techniques for statistical classification and
regression. The techniques are applied to data from a life assurance environment for a
binary classification problem and a regression problem. In the classification case the
problem is the prediction of policy lapses using a variety of input variables, while in
the regression case the goal is to estimate the income of clients from these variables.
The performance of the support vector machine is compared to that of discriminant
analysis and classification trees in the case of classification, and to that of multiple
linear regression and regression trees in regression, and it is found that support vector
machines generally perform well compared to the traditional techniques. / AFRIKAANSE OPSOMMING: Sedert die bekendstelling van die ondersteuningspuntalgoritme in Boser et al. (1992),
het dit 'n populêre tegniek in 'n verskeidenheid masjienleerteorie applikasies geword.
Meer onlangs het die ondersteuningspuntalgoritme ook meer aandag in die statistiese
gemeenskap begin geniet as 'n tegniek vir klassifikasie en regressie. In hierdie tesis
word ondersteuningspuntalgoritmes vergelyk met meer tradisionele tegnieke vir
statistiese klassifikasie en regressie. Die tegnieke word toegepas op data uit 'n
lewensversekeringomgewing vir 'n binêre klassifikasie probleem sowel as 'n
regressie probleem. In die klassifikasiegeval is die probleem die voorspelling van
polisvervallings deur 'n verskeidenheid invoer veranderlikes te gebruik, terwyl in die
regressiegeval gepoog word om die inkomste van kliënte met behulp van hierdie
veranderlikes te voorspel. Die resultate van die ondersteuningspuntalgoritme word
met dié van diskriminant analise en klassifikasiebome vergelyk in die
klassifikasiegeval, en met veelvoudige linêere regressie en regressiebome in die
regressiegeval. Die gevolgtrekking is dat ondersteuningspuntalgoritmes oor die
algemeen goed vaar in vergelyking met die tradisionele tegnieke.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/49810 |
| Date | 04 1900 |
| Creators | Hechter, Trudie |
| Contributors | Steel, S. J., Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistical and Actuarial Science. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
| Format | 159 p. |
| Rights | Stellenbosch University |
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