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

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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Bibliographic Details
Main Authors: Lawry, Jonathan. editor., Shanahan, Jimi. editor., Ralescu, Anca L. editor., SpringerLink (Online service)
Format: Texto biblioteca
Language:eng
Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2003
Subjects: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 Modeling.,
Online Access:http://dx.doi.org/10.1007/b94063
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Summary: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.