A comparison of support vector machines and traditional techniques for statistical regression and classification

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dc.contributor.advisorSteel, S. J.
dc.contributor.authorHechter, Trudie
dc.contributor.otherStellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistical and Actuarial Science.en_ZA
dc.date.accessioned2012-08-27T11:33:06Z
dc.date.available2012-08-27T11:33:06Z
dc.date.issued2004-04
dc.descriptionThesis (MComm)--Stellenbosch University, 2004.en_ZA
dc.description.abstractENGLISH 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.en_ZA
dc.description.abstractAFRIKAANSE 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.af_ZA
dc.format.extent159 p.
dc.identifier.urihttp://hdl.handle.net/10019.1/49810
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectMathematical statistics -- Data processingen_ZA
dc.subjectMachine learningen_ZA
dc.subjectRegression analysisen_ZA
dc.subjectDissertations -- Statistics and actuarial scienceen_ZA
dc.subjectTheses -- Statistics and actuarial scienceen_ZA
dc.titleA comparison of support vector machines and traditional techniques for statistical regression and classificationen_ZA
dc.typeThesisen_ZA
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