Case-based Predictions: An Axiomatic Approach To Prediction, Classification And Statistical Learning
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Case-based Predictions: An Axiomatic Approach To Prediction, Classification And Statistical Learning

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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.
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Product details

  • Hardback | 348 pages
  • 157.48 x 228.6 x 22.86mm | 612.35g
  • Singapore, Singapore
  • English
  • 981436617X
  • 9789814366175
  • 3,195,756

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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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Table of contents

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.
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