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020 _a9783319237084
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024 7 _a10.1007/978-3-319-23708-4
_2doi
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072 7 _aUYA
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aUYA
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082 0 4 _a005.131
_223
245 1 0 _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.
250 _a1st ed. 2015.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aX, 211 p. 62 illus. in color.
_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 ;
_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 _aMachine theory.
650 0 _aArtificial intelligence.
650 0 _aComputer programming.
650 0 _aApplication software.
650 0 _aComputer science.
650 1 4 _aFormal Languages and Automata Theory.
650 2 4 _aArtificial Intelligence.
650 2 4 _aProgramming Techniques.
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aComputer Science Logic and Foundations of Programming.
650 2 4 _aTheory of Computation.
700 1 _aDavis, Jesse.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aRamon, Jan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319237077
776 0 8 _iPrinted edition:
_z9783319237091
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v9046
856 4 0 _uhttps://doi.org/10.1007/978-3-319-23708-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
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