@inproceedings{ecf44c12ba2345648933015592b37909,
title = "Transfer learning in probabilistic logic models",
abstract = "Several approaches to learning probabilistic logic programs have been proposed in the literature. However, most learning systems based on these approaches are not efficient for handling large practical problems (especially, in the case of structure learning). It has been a challenging issue to reduce the search space of candidate (probabilistic) logic programs. There is no exception for SLIPCOVER, a latest system for both parameter and structure learning of Logic Programs with Annotated Disjunction (LPADs). This paper presents a new algorithm T-LPAD for structure learning of LPADs by employing transfer learning. The new algorithm has been implemented and our experimental results show that T-LPAD outperforms SLIPCOVER (and SLIPCASE) for most benchmarks used in related systems.",
keywords = "Probabilistic logic programs, Transfer learning",
author = "Omran, {Pouya Ghiasnezhad} and Kewen Wang and Zhe Wang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 29th Australasian Joint Conference on Artificial Intelligence, AI 2016 ; Conference date: 05-12-2016 Through 08-12-2016",
year = "2016",
doi = "10.1007/978-3-319-50127-7_33",
language = "English",
isbn = "9783319501260",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "378--389",
editor = "Kang, {Byeong Ho} and Quan Bai",
booktitle = "AI 2016",
address = "Germany",
}