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Advances in Predictive Data Mining Methods

  • Se June Hong
  • Sholom M. Weiss
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1715)

Abstract

Predictive models have been widely used long before the development of the new field that we call data mining. Expanding application demand for data mining of ever increasing data warehouses, and the need for understandability of predictive models with increased accuracy of prediction, all have fueled recent advances in automated predictive methods. We first examine a few successful application areas and technical challenges they present. We discuss some theoretical developments in PAC learning and statistical learning theory leading to the emergence of support vector machines. We then examine some technical advances made in enhancing the performance of the models both in accuracy (boosting, bagging, stacking) and scalability of modeling through distributed model generation.

Keywords

Support Vector Machine Text Mining Fraud Detection Optimal Hyperplane Multiple Decision Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Se June Hong
    • 1
  • Sholom M. Weiss
    • 1
  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

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