000 | 03280nam a22005415i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/0-387-30262-X _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTJ210.2-211.495 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aTJFM1 _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aLawry, Jonathan. _eauthor. |
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245 | 1 | 0 |
_aModelling and Reasoning with Vague Concepts _h[electronic resource] / _cby Jonathan Lawry. |
264 | 1 |
_aBoston, MA : _bSpringer US, _c2006. |
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300 |
_aXXV, 246 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Computational Intelligence, _x1860-949X ; _v12 |
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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 |