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

Itzhak Gilboa and David Schmeidler

Post-Print from HAL

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.

Keywords: Case-Based Predictions; Axiomatic Approach; Prediction; Classification; Statistical Learning (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

Published in World Scientific Publishers, pp.NC, 2011, Economic Theory Series

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Book: Case-Based Predictions:An Axiomatic Approach to Prediction, Classification and Statistical Learning (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00756301

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().