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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
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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.
650		0	_aMachine theory.
650		0	_aComputer science.
650		0	_aCompilers (Computer programs).
650		0	_aApplication software.
650		0	_aComputer networks .
650	1	4	_aArtificial Intelligence.
650	2	4	_aFormal Languages and Automata Theory.
650	2	4	_aComputer Science Logic and Foundations of Programming.
650	2	4	_aCompilers and Interpreters.
650	2	4	_aComputer and Information Systems Applications.
650	2	4	_aComputer Communication Networks.
700	1		_aKazakov, Dimitar. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aErten, Can. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
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
856	4	0	_uhttps://doi.org/10.1007/978-3-030-49210-6
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