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| LEADER |
00000cam a2200000 i 4500 |
| 001 |
on1300781537 |
| 003 |
OCoLC |
| 005 |
20240909213021.0 |
| 006 |
m o d |
| 007 |
cr un|---aucuu |
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220301s2022 sz a o 101 0 eng d |
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|d OCLCQ
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| 020 |
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|a 9783030974541
|q (electronic bk.)
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| 020 |
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|a 3030974545
|q (electronic bk.)
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|z 9783030974534
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| 024 |
7 |
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|a 10.1007/978-3-030-97454-1
|2 doi
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| 035 |
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|a (OCoLC)1300781537
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| 050 |
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|a QA76.63
|b .I47 2021eb
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|a UYQ
|2 bicssc
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|a COM004000
|2 bisacsh
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|a UYQ
|2 thema
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| 049 |
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|a HCDD
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| 111 |
2 |
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|a ILP (Conference)
|n (30th :
|d 2021 :
|c Online)
|
| 245 |
1 |
0 |
|a Inductive logic programming :
|b 30th International Conference, ILP 2021, Virtual event, October 25-27, 2021, Proceedings /
|c Nikos Katzouris, Alexander Artikis (eds.).
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| 246 |
3 |
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|a ILP 2021
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| 264 |
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1 |
|a Cham, Switzerland :
|b Springer,
|c 2022.
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| 300 |
|
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|a 1 online resource (x, 283 pages) :
|b illustrations (some color).
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| 336 |
|
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|a text
|b txt
|2 rdacontent
|
| 337 |
|
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|a computer
|b c
|2 rdamedia
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| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 490 |
1 |
|
|a Lecture notes in artificial intelligence
|
| 490 |
1 |
|
|a Lecture notes in computer science ;
|v 13191
|
| 490 |
1 |
|
|a LNCS sublibrary, SL 7, Artificial intelligence
|
| 505 |
0 |
|
|a Embedding Models for Knowledge Graphs Induced by Clusters of Relations and Background Knowledge -- Fanizzi Automatic Conjecturing of P-Recursions Using Lifted Inference -- Machine learning of microbial interactions using Abductive ILP and Hypothesis Frequency/Compression Estimation -- Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification -- Reyes Synthetic Datasets and Evaluation Tools for Inductive Neural Reasoning -- Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design -- Non-Parametric Learning of Embeddings for Relational Data using Gaifman Locality Theorem -- Ontology Graph Embeddings and ILP for Financial Forecasting -- Transfer learning for boosted relational dependency networks through genetic algorithm -- Online Learning of Logic Based Neural Network Structures -- Programmatic policy extraction by iterative local search -- Mapping across relational domains for transfer learning with word embeddings-based similarity -- A First Step Towards Even More Sparse Encodings of Probability Distributions -- Feature Learning by Least Generalization -- Learning Logic Programs Using Neural Networks by Exploiting Symbolic Invariance -- Learning and revising dynamic temporal theories in the full Discrete Event Calculus -- Human-like rule learning from images using one-shot hypothesis derivation -- Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits -- A Simulated Annealing Meta-heuristic for Concept Learning in Description Logics.
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| 500 |
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|a Includes author index.
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| 520 |
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|a This book constitutes the refereed conference proceedings of the 30th International Conference on Inductive Logic Programming, ILP 2032, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 papers and 3 short papers presented were carefully reviewed and selected from 19 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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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed March 1, 2022).
|
| 650 |
|
0 |
|a Logic programming
|v Congresses.
|
| 650 |
|
0 |
|a Induction (Logic)
|v Congresses.
|
| 650 |
|
0 |
|a Machine learning
|v Congresses.
|
| 650 |
|
7 |
|a Induction (Logic)
|2 fast
|
| 650 |
|
7 |
|a Logic programming
|2 fast
|
| 650 |
|
7 |
|a Machine learning
|2 fast
|
| 655 |
|
7 |
|a proceedings (reports)
|2 aat
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| 655 |
|
7 |
|a Conference papers and proceedings
|2 fast
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| 655 |
|
7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
|
| 700 |
1 |
|
|a Katzouris, Nikos,
|e editor.
|0 (orcid)0000-0001-8804-470X
|1 https://orcid.org/0000-0001-8804-470X
|
| 700 |
1 |
|
|a Artikis, Alexander,
|e editor.
|1 https://orcid.org/0000-0001-6899-4599
|
| 830 |
|
0 |
|a Lecture notes in computer science.
|p Lecture notes in artificial intelligence.
|
| 830 |
|
0 |
|a Lecture notes in computer science ;
|v 13191.
|
| 830 |
|
0 |
|a LNCS sublibrary.
|n SL 7,
|p Artificial intelligence.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-97454-1
|y Click for online access
|
| 903 |
|
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|a SPRING-COMP2022
|
| 994 |
|
|
|a 92
|b HCD
|