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

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Lawry, Jonathan (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Shanahan, Jimi (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Ralescu, Anca (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003.
• Έκδοση: 1st ed. 2003.
• Σειρά: Lecture Notes in Artificial Intelligence ; 2873
• Θέματα: 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 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. 

 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.