Modelling with Words Learning, Fusion, and Reasoning within a Formal Linguistic Representation Framework /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Lawry, Jonathan (Editor, http://id.loc.gov/vocabulary/relators/edt), Shanahan, Jimi (Editor, http://id.loc.gov/vocabulary/relators/edt), Ralescu, Anca (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003.
• Edition: 1st ed. 2003.
• Series: Lecture Notes in Artificial Intelligence ; 2873
• Subjects: Artificial intelligence.; Computers.; Mathematical logic.; Database management.; Information storage and retrieval.; Computer simulation.; Artificial Intelligence.; Computation by Abstract Devices.; Mathematical Logic and Formal Languages.; Database Management.; Information Storage and Retrieval.; Simulation and Modeling.

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 analy...

Table of Contents:

• 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.