Titlebook: Inductive Logic Programming; 29th International C Dimitar Kazakov,Can Erten Conference proceedings 2020 Springer Nature Switzerland AG 2020

COAX

书目名称Inductive Logic Programming影响因子(影响力)




书目名称Inductive Logic Programming影响因子(影响力)学科排名




书目名称Inductive Logic Programming网络公开度




书目名称Inductive Logic Programming网络公开度学科排名




书目名称Inductive Logic Programming被引频次




书目名称Inductive Logic Programming被引频次学科排名




书目名称Inductive Logic Programming年度引用




书目名称Inductive Logic Programming年度引用学科排名




书目名称Inductive Logic Programming读者反馈




书目名称Inductive Logic Programming读者反馈学科排名

古老

Rapid Restart Hill Climbing for Learning Description Logic Concepts,r expansion by traversing the search tree in a hill climbing manner and rapidly restarts with one-step backtracking after each expansion. We provide an implementation of RRHC in the DL-Learner framework and compare its performance with CELOE using standard benchmarks.

炸坏

表被动

Towards Meta-interpretive Learning of Programming Language Semantics, scenario, including abstracting over function symbols, nonterminating examples, and learning non-observed predicates, and propose extensions to Metagol helpful for overcoming these challenges, which may prove useful in other domains.

牵索

Towards an ILP Application in Machine Ethics,on approach relies on the non-monotonic features of Answer Set Programming (ASP) and applies ILP. The approach is illustrated by means of examples taken from the preliminary tests conducted with a couple of state-of-the-art ILP algorithms for learning ASP rules.

NAVEN

宿醉

Learning Logic Programs from Noisy State Transition Data,sed to understand the underlying model. In this paper, we propose a Differentiable Learning from Interpretation Transition (.-LFIT) algorithm, that can simultaneously output logic programs fully explaining the state transitions, and also learn from data containing noise and error.

Density

使厌恶

0302-9743 xamples, 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..978-3-030-49209-0978-3-030-49210-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

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