Výsledky vyhledávání - "Logical and relational learning"

Zdroj: Leibniz International Proceedings in Informatics (LIPIcs) ; 33rd EACSL Annual Conference on Computer Science Logic (CSL 2025) ; https://hal.science/hal-04931324 ; 33rd EACSL Annual Conference on Computer Science Logic (CSL 2025), 2025, Amsterdam, Netherlands. ⟨10.4230/LIPIcs.CSL.2025.8⟩

Témata: monadic second-order definable concept learning, agnostic probably approximately correct learning, parameterized complexity, clique-width, fixed-parameter tractable, Boolean classification, supervised learning, monadic second-order logic, Theory of computation → Logic, Theory of computation → Complexity theory and logic, Theory of computation → Fixed parameter tractability, Computing methodologies → Logical and relational learning, Computing methodologies → Supervised learning, [INFO]Computer Science [cs]

Geografické téma: Amsterdam, Netherlands

Relation: info:eu-repo/semantics/altIdentifier/arxiv/1909.03820; ARXIV: 1909.03820

Dostupnost: https://hal.science/hal-04931324
https://doi.org/10.4230/LIPIcs.CSL.2025.8

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4

Learning Concepts Described By Weight Aggregation Logic

Autoři: van Bergerem, Steffen Schweikardt, Nicole

Zdroj: Saarbrücken/Wadern, Germany : Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH, Dagstuhl Publishing, Leibniz international proceedings in informatics 183, 10:1-10:18 (2021). doi:10.4230/LIPICS.CSL.2021.10 ; 29th EACSL Annual Conference on Computer Science Logic : CSL 2021, January 25-28, 2021, Ljubljana, Slovenia (virtual conference) / edited by Christel Baier, Jean Goubault-Larrecq ; 29th EACSL Annual Conference on Computer Science Logic : CSL 2021, January 25-28, 2021, Ljubljana, Slovenia (virtual conference) / edited by Christel Baier, Jean Goubault-Larrecq 29. EACSL Annual Conference on Computer Science Logic, CSL 2021, online, 2021-01-25 - 2021-01-28

Témata: Computing methodologies → Logical and relational learning, Computing methodologies → Supervised learning, Feferman-Vaught decomposition, Gaifman normal form, Theory of computation → Complexity theory and logic, Theory of computation → Logic, agnostic probably approximately correct learning, classification problems, first-order definable concept learning, first-order logic with counting, locality, weight aggregation logic

Geografické téma: DE

Relation: info:eu-repo/semantics/altIdentifier/issn/1868-8969; info:eu-repo/semantics/altIdentifier/isbn/978-3-95977-175-7

Dostupnost: https://publications.rwth-aachen.de/record/815149
https://publications.rwth-aachen.de/search?p=id:%22RWTH-2021-02474%22

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5

Learning Concepts Described By Weight Aggregation Logic

Autoři: Van Bergerem, Steffen Schweikardt, Nicole Steffen van Bergerem and Nicole Schweikardt

Přispěvatelé: Van Bergerem, Steffen Schweikardt, Nicole Steffen van Bergerem and Nicole Schweikardt

Zdroj: Saarbrücken/Wadern, Germany : Schloss Dagstuhl-Leibniz-Zentrum für Informatik GmbH, Dagstuhl Publishing, Leibniz international proceedings in informatics 183, 10:1-10:18 (2021). doi:10.4230/LIPICS.CSL.2021.10
29th EACSL Annual Conference on Computer Science Logic : CSL 2021, January 25-28, 2021, Ljubljana, Slovenia (virtual conference) / edited by Christel Baier, Jean Goubault-Larrecq
29th EACSL Annual Conference on Computer Science Logic : CSL 2021, January 25-28, 2021, Ljubljana, Slovenia (virtual conference) / edited by Christel Baier, Jean Goubault-Larrecq29. EACSL Annual Conference on Computer Science Logic, CSL 2021, online, 2021-01-25-2021-01-28

Témata: FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, agnostic probably approximately correct learning, Computing methodologies → Logical and relational learning, 0102 computer and information sciences, 01 natural sciences, Gaifman normal form, Theory of computation → Logic, Logic in Computer Science (cs.LO), Machine Learning (cs.LG), locality, Artificial Intelligence (cs.AI), first-order definable concept learning, weight aggregation logic, first-order logic with counting, ddc:004, 0101 mathematics, Theory of computation → Complexity theory and logic, Feferman-Vaught decomposition, classification problems, Computing methodologies → Supervised learning

Popis souboru: application/pdf

Přístupová URL adresa: http://arxiv.org/abs/2009.10574
https://drops.dagstuhl.de/opus/volltexte/2021/13444/
https://dblp.uni-trier.de/db/journals/corr/corr2009.html#abs-2009-10574
https://drops.dagstuhl.de/opus/volltexte/2021/13444/pdf/LIPIcs-CSL-2021-10.pdf/
http://dblp.uni-trier.de/db/journals/corr/corr2009.html#abs-2009-10574
https://arxiv.org/abs/2009.10574
https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CSL.2021.10
https://publications.rwth-aachen.de/record/815149

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6

Inducing Space Dirichlet Process Mixture Large-Margin Entity RelationshipInference in Knowledge Bases

Autoři: Chatzis, Sotirios P. Χατζής, Σωτήριος Π.

Přispěvatelé: Chatzis, Sotirios P. Χατζής, Σωτήριος Π.

Zdroj: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. :1311-1320

Témata: Nonparametric statistics, 02 engineering and technology, Electrical Engineering - Electronic Engineering - Information Engineering, Mathematics of computing, 01 natural sciences, Computing methodologies, Probability and statistics, Machine learning approaches, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Engineering and Technology, Logical and relational learning, Inductive logic learning, 0101 mathematics

Popis souboru: pdf

Přístupová URL adresa: https://doi.acm.org/10.1145/2806416.2806499
https://dblp.uni-trier.de/db/conf/cikm/cikm2015.html#Chatzis15
https://dl.acm.org/doi/pdf/10.1145/2806416.2806499
https://hdl.handle.net/20.500.14279/8579

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7

Embedding cardinality constraints in neural link predictors

Autoři: Muñoz, E Minervini, P Nickles, M

Zdroj: In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. (pp. pp. 2243-2250). The Association for Computing Machinery: New York (NY), USA. (2019)

Témata: Computing methodologies, Artificial intelligence, Knowledge representation and reasoning, Semantic networks, Machine learning, Machine learning approaches, Logical and relational learning, Statistical relational learning

Popis souboru: text

Relation: https://discovery.ucl.ac.uk/id/eprint/10075181/8/Minervini_Embedding%20cardinality%20constraints%20in%20neural%20link%20predictors_AAM.pdf; https://discovery.ucl.ac.uk/id/eprint/10075181/

Dostupnost: https://discovery.ucl.ac.uk/id/eprint/10075181/8/Minervini_Embedding%20cardinality%20constraints%20in%20neural%20link%20predictors_AAM.pdf
https://discovery.ucl.ac.uk/id/eprint/10075181/

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8

kLog: A language for logical and relational learning with kernels (extended abstract)

Autoři: Frasconi, Paolo Costa, Fabrizio De Raedt, Luc a další

Přispěvatelé: Frasconi, Paolo Costa, Fabrizio De Raedt, Luc a další

Zdroj: ISBN:9781577357384 ; ISSN:1045-0823 ; Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), vol. 2015-January, (4183-4187) ; International Joint Conference on Artificial Intelligence (IJCAI-15), Buenos Aires, Argentina, 25-30 July 2015.

Témata: logical and relational learning, statistical relational learning, kernel-based learning, Prolog, deductive database, graph kernel, Science & Technology, Technology, Computer Science, Artificial Intelligence, Interdisciplinary Applications