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Books Year : 2011

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

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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.
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Dates and versions

hal-00756301 , version 1 (22-11-2012)

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  • HAL Id : hal-00756301 , version 1

Cite

Itzhak Gilboa, David Schmeidler. Case-Based Predictions: An Axiomatic Approach to Prediction, Classification and Statistical Learning. World Scientific Publishers, pp.NC, 2011, Economic Theory Series. ⟨hal-00756301⟩

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