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A comparison of support vector machines and traditional techniques for statistical regression and classification
Thesis (MComm)--Stellenbosch University, 2004.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2012
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| _version_ | 1867614073989890048 |
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| access_status_str | Open Access |
| author | Hechter, Trudie |
| author2 | Steel, S. J. |
| author_browse | Hechter, Trudie Steel, S. J. |
| author_facet | Steel, S. J. Hechter, Trudie |
| author_sort | Hechter, Trudie |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MComm)--Stellenbosch University, 2004. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/49810 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:46:15.146Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2012 |
| publishDateRange | 2012 |
| publishDateSort | 2012 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/49810 A comparison of support vector machines and traditional techniques for statistical regression and classification Hechter, Trudie Steel, S. J. Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistical and Actuarial Science. Mathematical statistics -- Data processing Machine learning Regression analysis Dissertations -- Statistics and actuarial science Theses -- Statistics and actuarial science 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. Masters 2012-08-27T11:33:06Z 2012-08-27T11:33:06Z 2004-04 Thesis http://hdl.handle.net/10019.1/49810 en_ZA Stellenbosch University 159 p. application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Mathematical statistics -- Data processing Machine learning Regression analysis Dissertations -- Statistics and actuarial science Theses -- Statistics and actuarial science Hechter, Trudie A comparison of support vector machines and traditional techniques for statistical regression and classification |
| title | A comparison of support vector machines and traditional techniques for statistical regression and classification |
| title_full | A comparison of support vector machines and traditional techniques for statistical regression and classification |
| title_fullStr | A comparison of support vector machines and traditional techniques for statistical regression and classification |
| title_full_unstemmed | A comparison of support vector machines and traditional techniques for statistical regression and classification |
| title_short | A comparison of support vector machines and traditional techniques for statistical regression and classification |
| title_sort | comparison of support vector machines and traditional techniques for statistical regression and classification |
| topic | Mathematical statistics -- Data processing Machine learning Regression analysis Dissertations -- Statistics and actuarial science Theses -- Statistics and actuarial science |
| url | http://hdl.handle.net/10019.1/49810 |
| work_keys_str_mv | AT hechtertrudie acomparisonofsupportvectormachinesandtraditionaltechniquesforstatisticalregressionandclassification AT hechtertrudie comparisonofsupportvectormachinesandtraditionaltechniquesforstatisticalregressionandclassification |