Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-...

Full description

Saved in:

Bibliographic Details
Published in:	Machine learning Vol. 94; no. 1; pp. 81 - 104
Main Authors:	França, Manoel V. M., Zaverucha, Gerson, d’Avila Garcez, Artur S.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.01.2014 Springer Nature B.V
Subjects:	Accuracy Artificial Intelligence Computer Science Control Learning Logic Logic programming Mathematical analysis Mechatronics Natural Language Processing (NLP) Neural networks Relational data bases Robotics Simulation and Modeling Tasks Vectors (mathematics) Relational learning Propositionalization Inductive logic programming Neural-symbolic integration Artificial neural networks
ISSN:	0885-6125, 1573-0565
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!