Abstract
Support vector machines are not only promising for solving pattern recognition tasks but have also produced several successful applications in medical diagnostics and object detection to date. So it is just natural to check whether this methodology might also be a helpful tool for classification in marketing and especially in sales force management. To answer this question both strengths and weaknesses of support vector machines in marketing data analysis are investigated exemplarily with special attention to the problem of selecting appropriate kernel functions and determining the belonging parameters. Difficulties arising in this context are illustrated by means of real data from health care.
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Monien, K., Decker, R. (2005). Strengths and Weaknesses of Support Vector Machines Within Marketing Data Analysis. In: Baier, D., Wernecke, KD. (eds) Innovations in Classification, Data Science, and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26981-9_41
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DOI: https://doi.org/10.1007/3-540-26981-9_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23221-6
Online ISBN: 978-3-540-26981-6
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