Lifted discriminative learning of probabilistic logic programs

Probabilistic logic programming (PLP) provides a powerful tool for reasoning with uncertain relational models. However, learning probabilistic logic programs is expensive due to the high cost of inference. Among the proposals to overcome this problem, one of the most promising is lifted inference. I...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Machine learning Ročník 108; číslo 7; s. 1111 - 1135
Hlavní autoři:	Nguembang Fadja, Arnaud, Riguzzi, Fabrizio
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.07.2019 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Computer Science Control Inference Logic programming Logic programs Machine learning Mathematical models Mechatronics Natural Language Processing (NLP) Parameters Robotics Simulation and Modeling Software Special Issue of the Inductive Logic Programming (ILP) Expectation maximization Lifted inference Probabilistic inductive logic programming Statistical relational learning Probabilistic logic programming
ISSN:	0885-6125, 1573-0565
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!