Learning MDL logic programs from noisy data
Machine Learning
2023-08-21 v1 Logic in Computer Science
Abstract
Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise.
Cite
@article{arxiv.2308.09393,
title = {Learning MDL logic programs from noisy data},
author = {Céline Hocquette and Andreas Niskanen and Matti Järvisalo and Andrew Cropper},
journal= {arXiv preprint arXiv:2308.09393},
year = {2023}
}
Comments
arXiv admin note: text overlap with arXiv:2206.01614