Learning an Approximation to Inductive Logic Programming Clause Evaluation
By Frank DiMaio, Jude W. Shavlik. 2004
19 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- A.S. d'Avila Garcez
- Luciano Serafini
- Michael Spranger
- L. C. Lamb
- Son Dinh Tran
- Marco Gori
- Gerson Zaverucha
- Manoel Vitor Macedo França
In Computational Theory and Mathematics
- A.S. d'Avila Garcez
- Marco Gori
- L. C. Lamb
- Luciano Serafini
- Michael Spranger
- Gerson Zaverucha
- Son Dinh Tran
- Manoel Vitor Macedo França
In Information Systems
- Luciano Serafini
- A.S. d'Avila Garcez
- Michael Spranger
- L. C. Lamb
- Son Dinh Tran
- Marco Gori
- Gerson Zaverucha
- Manoel Vitor Macedo França
Citations in the corpus
- Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, background): “Early work from [11] employs prositionalisation and feedforward neural networks to learn a clause evaluation function which helps improve the efficiency in exploring large hypothesis spaces.”
- Fast relational learning using bottom clause propositionalization with artificial neural networks (2013, methodology): “BCP uses bottom clauses for propositionalization because they carry semantic meaning, and because bottom clause literals can be used directly as features in a truth-table, simplifying the feature extraction process (Muggleton and Tamaddoni-Nezhad 2008; DiMaio and Shavlik 2004; Pitangui and Zaverucha”