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200 10$aInductive Logic Programming$b[electronic resource] $e29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3?5, 2019, Proceedings /$fedited by Dimitar Kazakov, Can Erten
205   $a1st ed. 2020.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020.
215   $a1 online resource (154 pages)
225 1 $aLecture Notes in Artificial Intelligence ;$v11770
311   $a3-030-49209-5 
327   $aCONNER: A Concurrent ILP Learner in Description Logic -- Towards Meta-interpretive Learning of Programming Language Semantics -- Towards an ILP Application in Machine Ethics -- On the Relation Between Loss Functions and T-Norms -- Rapid Restart Hill Climbing for Learning Description Logic Concepts -- Neural Networks for Relational Data -- Learning Logic Programs from Noisy State Transition Data -- A New Algorithm for Computing Least Generalization of a Set of Atoms -- LazyBum: Decision Tree Learning Using Lazy Propositionalization -- Weight Your Words: the Effect of Different Weighting Schemes on Wordification Performance -- Learning Probabilistic Logic Programs over Continuous Data.
330   $aThis book constitutes the refereed conference proceedings of the 29th International Conference on Inductive Logic Programming, ILP 2019, held in Plovdiv, Bulgaria, in September 2019. The 11 papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.
410  0$aLecture Notes in Artificial Intelligence ;$v11770
606   $aArtificial intelligence
606   $aMathematical logic
606   $aComputer logic
606   $aProgramming languages (Electronic computers)
606   $aApplication software
606   $aComputers
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048
606   $aLogics and Meanings of Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/I1603X
606   $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037
606   $aComputer Applications$3https://scigraph.springernature.com/ontologies/product-market-codes/I23001
606   $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008
615  0$aArtificial intelligence.
615  0$aMathematical logic.
615  0$aComputer logic.
615  0$aProgramming languages (Electronic computers).
615  0$aApplication software.
615  0$aComputers.
615 14$aArtificial Intelligence.
615 24$aMathematical Logic and Formal Languages.
615 24$aLogics and Meanings of Programs.
615 24$aProgramming Languages, Compilers, Interpreters.
615 24$aComputer Applications.
615 24$aInformation Systems and Communication Service.
676   $a005.115
702   $aKazakov$b Dimitar$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aErten$b Can$4edt$4http://id.loc.gov/vocabulary/relators/edt
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906   $aBOOK
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996   $aInductive Logic Programming$9772244
997   $aUNISA