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Record Nr. |
UNINA9910410058603321 |
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Titolo |
Inductive Logic Programming : 29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3–5, 2019, Proceedings / / edited by Dimitar Kazakov, Can Erten |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (154 pages) |
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Collana |
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Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 11770 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Machine theory |
Computer science |
Compilers (Computer programs) |
Application software |
Computer networks |
Artificial Intelligence |
Formal Languages and Automata Theory |
Computer Science Logic and Foundations of Programming |
Compilers and Interpreters |
Computer and Information Systems Applications |
Computer Communication Networks |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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CONNER: 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 |
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Wordification Performance -- Learning Probabilistic Logic Programs over Continuous Data. |
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Sommario/riassunto |
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This 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. |
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