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Modelling with Words [electronic resource] :Learning, Fusion, and Reasoning within a Formal Linguistic Represntation Framework /

Contributor(s): Lawry, Jonathan [editor.] | Shanahan, Jimi [editor.] | Ralescu, Anca L [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 2873Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2003.Description: XII, 506 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540399063.Subject(s): Computer science | Computers | Mathematical logic | Database management | Information storage and retrieval | Artificial intelligence | Computer simulation | Computer Science | Artificial Intelligence (incl. Robotics) | Computation by Abstract Devices | Mathematical Logic and Formal Languages | Database Management | Information Storage and Retrieval | Simulation and ModelingOnline resources: Click here to access online
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.
In: Springer eBooksSummary: 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.
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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.

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.

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