
Case-based Predictions: An Axiomatic Approach To Prediction, Classification And Statistical Learning
An Axiomatic Approach To Prediction, Classification and Statistical Learning
World Scientific Publishing Co Pte Ltd
Published on 27. April 2012
Book
Hardback
348 pages
978-981-4366-17-5 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Singapore
Singapore
Target group
College/higher education
Professional and scholarly
Graduate, research students and professionals in the field of economic theory and decision theory.
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 23 mm
Weight
655 gr
ISBN-13
978-981-4366-17-5 (9789814366175)
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Schweitzer Classification
Persons
Author
Tel-aviv Univ, Israel & Hec, Paris, France
Tel-aviv Univ, Israel & Ohio State Univ, Usa
Content
Case-Based Decision Theory; Act Similarity in Case-Based Decision Theory; A Cognitive Foundation of Probability; Inductive Inference: An Axiomatic Approach; Expected Utility in the Context of a Game; Subjective Distributions; Probabilities as Similarity-Weighted Frequencies; Fact-Free Learning; Empirical Similarity; Axiomatization of an Exponential Similarity Function; On the Definition of Objective Probabilities by Empirical Similarity; Likelihood and Simplicity: An Axiomatic Approach.