Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Niels Pahlavi"'
Publikováno v:
Machine Learning. 94:25-49
Despite early interest Predicate Invention has lately been under-explored within ILP. We develop a framework in which predicate invention and recursive generalisations are implemented using abduction with respect to a meta-interpreter. The approach i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41a4bc07f2438b2500cf0b37be04d6c6
https://doi.org/10.1007/s10994-013-5358-3
https://doi.org/10.1007/s10994-013-5358-3
Autor:
Niels Pahlavi, Stephen Muggleton
Publikováno v:
Inductive Logic Programming ISBN: 9783642212949
ILP
ILP
Learning first-order recursive theories remains a difficult learning task in a normal Inductive Logic Programming (ILP) setting, although numerous approaches have addressed it; using Higher-order Logic (HOL) avoids having to learn recursive clauses f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::de1170571284f9e7de9f87a30716809c
https://doi.org/10.1007/978-3-642-21295-6_23
https://doi.org/10.1007/978-3-642-21295-6_23
Autor:
Stephen Muggleton, Niels Pahlavi
Publikováno v:
Inductive Logic Programming ISBN: 9783540738466
ILP
ILP
Probabilistic Logic Learning (PLL) aims at learning probabilistic logical frameworks on the basis of data. Such frameworks combine expressive knowledge representation formalisms with reasoning mechanisms grounded in probability theory. Numerous frame
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07cee884196f82c31d74d7a5c758fec5
https://doi.org/10.1007/978-3-540-73847-3_33
https://doi.org/10.1007/978-3-540-73847-3_33