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020 _a9783030492106
_9978-3-030-49210-6
024 7 _a10.1007/978-3-030-49210-6
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aInductive Logic Programming
_h[electronic resource] :
_b29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3-5, 2019, Proceedings /
_cedited by Dimitar Kazakov, Can Erten.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aIX, 145 p. 125 illus., 19 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 ;
_v11770
505 0 _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.
520 _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.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine theory.
_989889
650 0 _aComputer science.
_99832
650 0 _aCompilers (Computer programs).
_93350
650 0 _aApplication software.
_989890
650 0 _aComputer networks .
_931572
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aFormal Languages and Automata Theory.
_989891
650 2 4 _aComputer Science Logic and Foundations of Programming.
_942203
650 2 4 _aCompilers and Interpreters.
_931853
650 2 4 _aComputer and Information Systems Applications.
_989892
650 2 4 _aComputer Communication Networks.
_989893
700 1 _aKazakov, Dimitar.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_989894
700 1 _aErten, Can.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_989895
710 2 _aSpringerLink (Online service)
_989896
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030492090
776 0 8 _iPrinted edition:
_z9783030492113
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v11770
_989897
856 4 0 _uhttps://doi.org/10.1007/978-3-030-49210-6
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912 _aZDB-2-SXCS
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