Abstract
Traditionally there have been two paradigms of statistical analysis — classical and Bayesian. Machine learning is essentially a third paradigm, based on algorithms that rely heavily on the speed of modern computing to derive “decision rules” that predict customer behavior. We discuss several machine learning techniques, including covering algorithms, instance-based learning, genetic algorithms, Bayesian networks, support vector machines, and committee machine methods such as bagging and boosting.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Blattberg, R.C., Kim, BD., Neslin, S.A. (2008). Machine Learning. In: Database Marketing. International Series in Quantitative Marketing, vol 18. Springer, New York, NY. https://doi.org/10.1007/978-0-387-72579-6_19
Download citation
DOI: https://doi.org/10.1007/978-0-387-72579-6_19
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-72578-9
Online ISBN: 978-0-387-72579-6
eBook Packages: Business and EconomicsBusiness and Management (R0)