000 03280nam a22005415i 4500
001 978-0-387-30262-1
003 DE-He213
005 20170628033301.0
007 cr nn 008mamaa
008 100301s2006 xxu| s |||| 0|eng d
020 _a9780387302621
_9978-0-387-30262-1
024 7 _a10.1007/0-387-30262-X
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aLawry, Jonathan.
_eauthor.
245 1 0 _aModelling and Reasoning with Vague Concepts
_h[electronic resource] /
_cby Jonathan Lawry.
264 1 _aBoston, MA :
_bSpringer US,
_c2006.
300 _aXXV, 246 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v12
505 0 _aVague Concepts and Fuzzy Sets -- Label Semantics -- Multi-Dimensional and Multi-Instance Label Semantics -- Information from Vague Concepts -- Learning Linguistic Models from Data -- Fusing Knowledge and Data -- Non-Additive Appropriateness Measures.
520 _aVagueness is central to the flexibility and robustness of natural language descriptions. Vague concepts are robust to the imprecision of our perceptions, while still allowing us to convey useful, and sometimes vital, information. The study of vagueness in Artificial Intelligence (AI) is therefore motivated by the desire to incorporate this robustness and flexibility into intelligent computer systems. Such a goal, however, requires a formal model of vague concepts that will allow us to quantify and manipulate the uncertainty resulting from their use as a means of passing information between autonomous agents. This volume outlines a formal representation framework for modelling and reasoning with vague concepts in Artificial Intelligence. The new calculus has many applications, especially in automated reasoning, learning, data analysis and information fusion. This book gives a rigorous introduction to label semantics theory, illustrated with many examples, and suggests clear operational interpretations of the proposed measures. It also provides a detailed description of how the theory can be applied in data analysis and information fusion based on a range of benchmark problems.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 0 _aOptical pattern recognition.
650 0 _aMathematics.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aSystems and Information Theory in Engineering.
650 2 4 _aPattern Recognition.
650 2 4 _aInformation and Communication, Circuits.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aMathematical Logic and Formal Languages.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387290560
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v12
856 4 0 _uhttp://dx.doi.org/10.1007/0-387-30262-X
912 _aZDB-2-ENG
999 _c14477
_d14477