000 | 03914nam a22006495i 4500 | ||
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001 | 978-3-319-50137-6 | ||
003 | DE-He213 | ||
005 | 20200421112050.0 | ||
007 | cr nn 008mamaa | ||
008 | 161202s2016 gw | s |||| 0|eng d | ||
020 |
_a9783319501376 _9978-3-319-50137-6 |
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024 | 7 |
_a10.1007/978-3-319-50137-6 _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 |
245 | 1 | 0 |
_aData Mining and Constraint Programming _h[electronic resource] : _bFoundations of a Cross-Disciplinary Approach / _cedited by Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi. |
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016. |
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300 |
_aXII, 349 p. 73 illus. _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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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Computer Science, _x0302-9743 ; _v10101 |
|
505 | 0 | _aIntroduction to Combinatorial Optimisation in Numberjack -- Data Mining and Constraints: An Overview -- New Approaches to Constraint Acquisition -- ModelSeeker: Extracting Global Constraint Models from Positive Examples -- Learning Constraint Satisfaction Problems: An ILP Perspective -- Learning Modulo Theories -- Algorithm Selection for Combinatorial Search Problems: A Survey -- Adapting Consistency in Constraint Solving -- Modeling in MiningZinc -- Partition-Based Clustering Using Constraint Optimisation -- The Inductive Constraint Programming Loop -- ICON Loop Carpooling Show Case -- ICON Loop Health Show Case -- ICON Loop Energy Show Case. | |
520 | _aA successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge. This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on "Inductive Constraint Programming" and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases. . | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aAlgorithms. | |
650 | 0 | _aDatabase management. | |
650 | 0 | _aData mining. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aComputer simulation. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aInformation Systems Applications (incl. Internet). |
650 | 2 | 4 | _aSimulation and Modeling. |
650 | 2 | 4 | _aAlgorithm Analysis and Problem Complexity. |
650 | 2 | 4 | _aDatabase Management. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
700 | 1 |
_aBessiere, Christian. _eeditor. |
|
700 | 1 |
_aDe Raedt, Luc. _eeditor. |
|
700 | 1 |
_aKotthoff, Lars. _eeditor. |
|
700 | 1 |
_aNijssen, Siegfried. _eeditor. |
|
700 | 1 |
_aO'Sullivan, Barry. _eeditor. |
|
700 | 1 |
_aPedreschi, Dino. _eeditor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319501369 |
830 | 0 |
_aLecture Notes in Computer Science, _x0302-9743 ; _v10101 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-50137-6 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-LNC | ||
942 | _cEBK | ||
999 |
_c57168 _d57168 |