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Modelling and Reasoning with Vague Concepts

Aus der Reihe Studies in Computational Intelligence
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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

11.01.2006

Verlag

Springer Us

Seitenzahl

246

Maße (L/B/H)

24,4/16,4/2,2 cm

Gewicht

598 g

Auflage

2006 edition

Sprache

Englisch

ISBN

978-0-387-29056-0

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

11.01.2006

Verlag

Springer Us

Seitenzahl

246

Maße (L/B/H)

24,4/16,4/2,2 cm

Gewicht

598 g

Auflage

2006 edition

Sprache

Englisch

ISBN

978-0-387-29056-0

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Modelling and Reasoning with Vague Concepts
  • Produktbild: Modelling and Reasoning with Vague Concepts
  • List of Figures
    Preface
    Acknowledgments
    Foreword

    1: Introduction

    2: Vague Concepts And Fuzzy Sets
    2.1 Fuzzy Set Theory
    2.2 Functionality and Truth-Functionality
    2.3 Operational Semantics for Membership Functions

    3: Label Semantics
    3.1 Introduction and Motivation
    3.2 Appropriateness Measures and Mass Assignments on Labels
    3.3 Label Expressions and lambda-Sets
    3.4 A Voting Model for Label Semantics
    3.5 Properties of Appropriateness Measures
    3.6 Functional Label Semantics
    3.7 Relating Appropriateness Measures to Dempster-Shafer Theory
    3.8 Mass Selection Functions based on t-norms
    3.9 Alternative Mass Selection Functions
    3.10 An Axiomatic Approach to Appropriateness Measures
    3.11 Label Semantics as a Model of Assertions
    3.12 Relating Label Semantics to Existing Theories of Vagueness

    4: Multi-Dimensional And Multi-Instance Label Semantics
    4.1 Descriptions Based on Many Attributes
    4.2 Multi-dimensional Label Expressions and A-Sets
    4.3 Properties of Multi-dimensional Appropriateness Measures
    4.4 Describing Multiple Objects
    5: Information From Vague Concepts
    5.1 Possibility Theory
    5.2 The Probability of Fuzzy Sets
    5.3 Bayesian Conditioning in Label Semantics
    5.4 Possibilistic Conditioning in Label Semantics
    5.5 Matching Concepts
    5.6 Conditioning From Mass Assignments in Label Semantics

    6: Learning Linguistic Models From Data
    6.1 Defining Labels for Data Modelling
    6.2 Bayesian Classification using Mass Relations
    6.3 Prediction using Mass Relations
    6.4 Qualitative Information from Mass Relations
    6.5 Learning Linguistic Decision Trees
    6.6 Prediction using Decision Trees
    6.7 Query evaluation and Inference from Linguistic Decision Trees

    7: Fusing Knowledge And Data
    7.1 From Label Expressions to Informative Priors
    7.2 Combining Label Expressions with Data

    8: Non-Additive Appropriateness Measures
    8.1 Properties of Generalised Appropriateness Measures
    8.2 Possibilstic Appropriateness Measures
    8.3 An Axiomatic Approach to Generalised Appropriateness Measures
    8.4 The Law of Excluded Middle

    References
    Index