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001 978-3-540-39906-3
003 DE-He213
005 20240423132553.0
007 cr nn 008mamaa
008 121227s2003 gw | s |||| 0|eng d
020 _a9783540399063
_9978-3-540-39906-3
024 7 _a10.1007/b94063
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aModelling with Words
_h[electronic resource] :
_bLearning, Fusion, and Reasoning within a Formal Linguistic Representation Framework /
_cedited by Jonathan Lawry, Jimi Shanahan, Anca Ralescu.
250 _a1st ed. 2003.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2003.
300 _aXII, 506 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2873
505 0 _aRandom 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.
520 _aModelling 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.
650 0 _aArtificial intelligence.
650 0 _aComputer science.
650 0 _aMachine theory.
650 0 _aDatabase management.
650 0 _aInformation storage and retrieval systems.
650 0 _aComputer simulation.
650 1 4 _aArtificial Intelligence.
650 2 4 _aTheory of Computation.
650 2 4 _aFormal Languages and Automata Theory.
650 2 4 _aDatabase Management.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aComputer Modelling.
700 1 _aLawry, Jonathan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aShanahan, Jimi.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aRalescu, Anca.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540204879
776 0 8 _iPrinted edition:
_z9783662197721
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2873
856 4 0 _uhttps://doi.org/10.1007/b94063
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
912 _aZDB-2-BAE
942 _cSPRINGER
999 _c189236
_d189236