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

COAX

書目名稱Inductive Logic Programming影響因子(影響力)




書目名稱Inductive Logic Programming影響因子(影響力)學科排名




書目名稱Inductive Logic Programming網(wǎng)絡(luò)公開度




書目名稱Inductive Logic Programming網(wǎng)絡(luò)公開度學科排名




書目名稱Inductive Logic Programming被引頻次




書目名稱Inductive Logic Programming被引頻次學科排名




書目名稱Inductive Logic Programming年度引用




書目名稱Inductive Logic Programming年度引用學科排名




書目名稱Inductive Logic Programming讀者反饋




書目名稱Inductive Logic Programming讀者反饋學科排名

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.

炸壞

表被動

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.

牽索

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

使厭惡

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

內(nèi)向者