Machine Learning

  • Robert C. Blattberg
  • Byung-Do Kim
  • Scott A. Neslin
Part of the International Series in Quantitative Marketing book series (ISQM, volume 18)


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.


Support Vector Machine Bayesian Network Training Instance Service Recovery Rule Induction 
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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Robert C. Blattberg
    • 1
    • 2
  • Byung-Do Kim
    • 3
  • Scott A. Neslin
    • 4
  1. 1.Kellogg School of ManagementNorthwestern UniversityEvanstonUSA
  2. 2.Tepper School of BusinessCarnegie-Mellon UniversityPittsburghUSA
  3. 3.Graduate School of BusinessSeoul National UniversitySeoulKorea
  4. 4.Tuck School of BusinessDartmouth CollegeHanoverUSA

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