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Inductive Logic Programming: 30th International Conference, ILP 2021, Virtual Event, October 2527, 2021, Proceedings 1st ed. 2022 [Pehme köide]

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  • Formaat: Paperback / softback, 283 pages, kõrgus x laius: 235x155 mm, kaal: 456 g, 40 Illustrations, color; 21 Illustrations, black and white; X, 283 p. 61 illus., 40 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Artificial Intelligence 13191
  • Ilmumisaeg: 24-Feb-2022
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030974537
  • ISBN-13: 9783030974534
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Inductive Logic Programming: 30th International Conference, ILP 2021, Virtual Event, October 2527, 2021, Proceedings 1st ed. 2022Suurem pilt
  • Formaat: Paperback / softback, 283 pages, kõrgus x laius: 235x155 mm, kaal: 456 g, 40 Illustrations, color; 21 Illustrations, black and white; X, 283 p. 61 illus., 40 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Artificial Intelligence 13191
  • Ilmumisaeg: 24-Feb-2022
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030974537
  • ISBN-13: 9783030974534
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030974534.html
Märksõnad:
This book constitutes the refereed conference proceedings of the 30th International Conference on Inductive Logic Programming, ILP 2021, 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.

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.