Modelling with Words : Learning, Fusion, and Reasoning within a Formal Lingustic Representation Framework (Lecture Notes in Computer Science Vol.2873) (2003. XIII, 229 p.)

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Modelling with Words : Learning, Fusion, and Reasoning within a Formal Lingustic Representation Framework (Lecture Notes in Computer Science Vol.2873) (2003. XIII, 229 p.)

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基本説明

Subseries: Lecture Notes in Artificial Intelligence.

Description


(Text)

Modelling with Words is an emerging modelling methodology closely related to the paradigm of Computing with Words introduced by Lotfi Zadeh.

This book is an authoritative collection of key contributions to the new concept of Modelling with Words. A wide range of issues in systems modelling and analysis is presented, extending from conceptual graphs and fuzzy quantifiers to humanist computing and self-organizing maps. Among the core issues investigated are

- balancing predictive accuracy and high level transparency in learning

- scaling linguistic algorithms to high-dimensional data problems

- integrating linguistic expert knowledge with knowledge derived from data

- identifying sound and useful inference rules

- integrating fuzzy and probabilistic uncertainty in data modelling

(Table of content)
Random Set-Based Approaches for Modelling Fuzzy Operators.- A General Framework for Induction of Decision Trees under Uncertainty.- Combining Rule Weight Learning and Rule Selection to Obtain Simpler and More Accurate Linguistic Fuzzy Models.- Semantics-Preserving Dimensionality Reduction in Intelligent Modelling.- Conceptual Graphs for Modelling and Computing with Generally Quantified Statements.- Improvement of the Interpretability of Fuzzy Rule Based Systems: Quantifiers, Similarities and Aggregators.- Humanist Computing: Modelling with Words, Concepts, and Behaviours.- A Hybrid Framework Using SOM and Fuzzy Theory for Textual Classification in Data Mining.- Combining Collaborative and Content-Based Filtering Using Conceptual Graphs.- Random Sets and Appropriateness Degrees for Modelling with Labels.- Interpretability Issues in Fuzzy Genetics-Based Machine Learning for Linguistic Modelling.

Table of Contents

    Random Set-Based Approaches for Modelling      1  (26)
Fuzzy Operators
Felix Diaz-Hermida
Purification Carinena
Alberto Bugarin
Senen Barro
A General Framework for Induction of 26 (18)
Decision Trees under Uncertainty
Enric Hernandez
Jordi Recasens
Combining Rule Weight Learning and Rule 44 (20)
Selection to Obtain Simpler and More
Accurate Linguistic Fuzzy Models
Rafael Alcala
Oscar Cordon
Francisco Herrera
Semantics-Preserving Dimensionality 64 (16)
Reduction in Intelligent Modelling
Qiang Shen
Conceptual Graphs for Modelling and 80 (22)
Computing with Generally Quantified
Statements
Tru H. Cao
Improvement of the Interpretability of 102(22)
Fuzzy Rule Based Systems: Quantifiers,
Similarities and Aggregators
Anne Laurent
Christophe Marsala
Bernadette Bouchon-Meunier
Humanist Computing: Modelling with Words, 124(29)
Concepts, and Behaviours
Jonathan Rossiter
A Hybrid Framework Using SOM and Fuzzy 153(15)
Theory for Textual Classification in Data
Mining
Yi-Ping Phoebe Chen
Combining Collaborative and Content-Based 168(18)
Filtering Using Conceptual Graphs
Patrick Paulson
Aimilia Tzanavari
Random Sets and Appropriateness Degrees for 186(23)
Modelling with Labels
Jonathan Lawry
Interpretability Issues in Fuzzy 209(20)
Genetics-Based Machine Learning for
Linguistic Modelling
Hisao Ishibuchi
Takashi Yamamoto
Author Index 229