Modelling with Words (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
506
Utgivningsdatum
2003-11-01
Upplaga
1991 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Lawry, Jonathan (ed.), Shanahan, Jimi (ed.), Ralescu, Anca (ed.)
Illustrationer
XII, 506 p.
Dimensioner
229 x 152 x 15 mm
Vikt
431 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISSN
0302-9743
ISBN
9783540204879
Modelling with Words (häftad)

Modelling with Words

Learning, Fusion, and Reasoning within a Formal Linguistic Representation Framework

Häftad Engelska, 2003-11-01
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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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Innehållsförteckning

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.