CASE-BASED PREDICTIONS

An Axiomatic Approach To Prediction, Classification And Statistical Learning

By: GILBOA ITZHAK

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Hardcover | 27 April 2012

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348 Pages

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22.86 x 15.24 x 2.06

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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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Foreword	p. v
Acknowledgments	p. vii
Introduction	p. xi
Case-Based Decision Theory	p. 1
Act Similarity in Case-Based Decision Theory	p. 43
Cognitive Foundations of Probability	p. 67
Inductive Inference: An Axiomatic Approach	p. 97
A Derivation of Expected Utility Maximization in the Context of a Game	p. 139
Subjective Distributions	p. 157
Probabilities as Similarity-Weighted Frequencies	p. 169
Fact-Free Learning	p. 185
Empirical Similarity	p. 211
Axiomatization of an Exponential Similarity Function	p. 245
On the Definition of Objective Probabilities by Empirical Similarity	p. 259
Simplicity and Likelihood: An Axiomatic Approach	p. 281
Table of Contents provided by Ingram. All Rights Reserved.

CASE-BASED PREDICTIONS

An Axiomatic Approach To Prediction, Classification And Statistical Learning

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

More in Economic Theory & Philosophy

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Capital in the Twenty-First Century

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The Mushroom at the End of the World

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Power and Progress

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The World According to Star Wars

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