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

Itzhak Gilboa and David Schmeidler

in World Scientific Books from World Scientific Publishing Co. Pte. Ltd.

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 Reasoning; Case-Based Decision Theory; Statistics; Decision Under Uncertainty (search for similar items in EconPapers)
Date: 2012
ISBN: 9789814366175
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https://www.worldscientific.com/worldscibooks/10.1142/8243 (text/html)
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Chapters in this book:

Ch 7 Probabilities as Similarity-Weighted Frequencies , pp 169-184

Antoine Billot, Itzhak Gilboa, Dov Samet and David Schmeidler

Ch 8 Fact-Free Learning , pp 185-210

Enriqueta Aragones, Itzhak Gilboa, Andrew Postlewaite and David Schmeidler

Ch 9 Empirical Similarity , pp 211-243

Itzhak Gilboa, Offer Lieberman and David Schmeidler

Ch 10 Axiomatization of an Exponential Similarity Function , pp 245-257

Antoine Billot, Itzhak Gilboa and David Schmeidler

Ch 11 On the Definition of Objective Probabilities by Empirical Similarity , pp 259-280

Itzhak Gilboa, Offer Lieberman and David Schmeidler

Related works:
Working Paper: Case-Based Predictions: An Axiomatic Approach to Prediction, Classification and Statistical Learning (2011)
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