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001 | 978-3-319-23708-4 | ||
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020 |
_a9783319237084 _9978-3-319-23708-4 |
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_a10.1007/978-3-319-23708-4 _2doi |
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_aInductive Logic Programming _h[electronic resource] : _b24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers / _cedited by Jesse Davis, Jan Ramon. |
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015. |
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300 |
_aX, 211 p. 62 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_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 ; _v9046 |
|
505 | 0 | _aReframing on Relational Data -- Inductive Learning using Constraint-driven Bias -- Nonmonotonic Learning in Large Biological Networks -- Construction of Complex Aggregates with Random Restart Hill-Climbing -- Logical minimisation of meta-rules within Meta-Interpretive Learning -- Goal and plan recognition via parse trees using prefix and infix probability computation -- Effectively creating weakly labeled training examples via approximate domain knowledge -- Learning Prime Implicant Conditions From Interpretation Transition -- Statistical Relational Learning for Handwriting Recognition -- The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions -- Towards machine learning of predictive models from ecological data -- PageRank, ProPPR, and Stochastic Logic Programs -- Complex aggregates over clusters of elements -- On the Complexity of Frequent Subtree Mining in Very Simple Structures. | |
520 | _aThis book constitutes the thoroughly refereed post-conference proceedings of the 24th International Conference on Inductive Logic Programming, ILP 2014, held in Nancy, France, in September 2014. The 14 revised papers presented were carefully reviewed and selected from 41 submissions. The papers focus on topics such as the inducing of logic programs, learning from data represented with logic, multi-relational machine learning, learning from graphs, and applications of these techniques to important problems in fields like bioinformatics, medicine, and text mining. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aComputer programming. | |
650 | 0 | _aComputers. | |
650 | 0 | _aComputer logic. | |
650 | 0 | _aMathematical logic. | |
650 | 0 | _aArtificial intelligence. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aMathematical Logic and Formal Languages. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aProgramming Techniques. |
650 | 2 | 4 | _aInformation Systems Applications (incl. Internet). |
650 | 2 | 4 | _aLogics and Meanings of Programs. |
650 | 2 | 4 | _aComputation by Abstract Devices. |
700 | 1 |
_aDavis, Jesse. _eeditor. |
|
700 | 1 |
_aRamon, Jan. _eeditor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319237077 |
830 | 0 |
_aLecture Notes in Computer Science, _x0302-9743 ; _v9046 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-23708-4 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-LNC | ||
942 | _cEBK | ||
999 |
_c53217 _d53217 |