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Case-Based Predictions: An Axiomatic Approach to Prediction, Classification and Statistical Learning

Author

Listed:
  • Itzhak Gilboa

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

  • David Schmeidler

Abstract

The book presents an axiomatic approach to the problems of prediction, classification, and statistical learning. Using methodologies from axiomatic decision theory, and, in particular, the authors' case-based decision theory, the present studies attempt to ask what inductive conclusions can be derived from existing databases. It is shown that simple consistency rules lead to similarity-weighted aggregation, akin to kernel-based methods. It is suggested that the similarity function be estimated from the data. The incorporation of rule-based reasoning is discussed.

Suggested Citation

  • Itzhak Gilboa & David Schmeidler, 2011. "Case-Based Predictions: An Axiomatic Approach to Prediction, Classification and Statistical Learning," Post-Print hal-00756301, HAL.
    • Handle: RePEc:hal:journl:hal-00756301
    as

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