Solving and interpreting binary classification problems in marketing with SVMs
Marketing problems often involve inary classification of customers into ``buyers'' versus ``non-buyers'' or ``prefers brand A'' versus ``prefers brand B''. These cases require binary classification models such as logistic regression, linear, and quadratic discriminant analysis. A promising recent technique for the binary classification problem is the Support Vector Machine (Vapnik (1995)), which has achieved outstanding results in areas ranging from Bioinformatics to Finance. In this paper, we compare the performance of the Support Vector Machine against standard binary classification techniques on a marketing data set and elaborate on the interpretation of the obtained results.
Year of publication: |
2005-11-09
|
---|---|
Authors: | Bioch, J.C. ; Groenen, P.J.F. ; Nalbantov, G.I. |
Institutions: | Erasmus University Rotterdam, Econometric Institute |
Saved in:
freely available
Extent: | application/pdf |
---|---|
Series: | Econometric Institute Report. - ISSN 1566-7294. |
Type of publication: | Book / Working Paper |
Notes: | The text is part of a series RePEc:dgr:eureir Number EI 2005-46 |
Source: |
Persistent link: https://www.econbiz.de/10005450848
Saved in favorites
Similar items by person
-
Nonlinear support vector machines through iterative majorization and I-splines
Groenen, P.J.F., (2006)
-
SVM-Maj: a majorization approach to linear support vector machines with different hinge errors
Groenen, P.J.F., (2007)
-
Nearest convex hull classification
Nalbantov, G.I., (2006)
- More ...