Template-Type: ReDIF-Paper 1.0
Author-Name: Bioch, J.C.
Author-Name-Last: Bioch
Author-Name-First: Cor
Author-Name: Groenen, P.J.F.
Author-Name-Last: Groenen
Author-Name-First: Patrick
Author-Person: pgr229
Author-Name: Nalbantov, G.I.
Author-Name-Last: Nalbantov
Author-Name-First: Georgi
Title: Solving and interpreting binary classification problems in marketing with SVMs
Abstract: 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.
Creation-Date: 2005-11-09
File-URL: https://repub.eur.nl/pub/7038/ei2005-46.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2005-46
Handle: RePEc:ems:eureir:7038