Modeling PU learning using probabilistic logic programming
Author:
Funder
Vlaamse Overheid
TAILOR
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s10994-023-06461-3.pdf
Reference31 articles.
1. Bekker, J., & Davis, J. (2018). Estimating the class prior in positive and unlabeled data through decision tree induction. In Proceedings of the AAAI conference on artificial intelligence (pp. 2712–2719).
2. Bekker, J., Robberechts, P., & Davis, J. (2019). Beyond the selected completely at random assumption for learning from positive and unlabeled data. In Proceedings of the joint European conference on machine learning and knowledge discovery in databases, pp. 71–85. Springer.
3. Bekker, J., & Davis, J. (2018). Positive and unlabeled relational classification through label frequency estimation. In N. Lachiche & C. Vrain (Eds.), Proceedings of the international conference on inductive logic programming (pp. 16–30).
4. Bekker, J., & Davis, J. (2020). Learning from positive and unlabeled data: A survey. Machine Learning, 109(4), 719–760.
5. Blanchard, G., Lee, G., & Scott, C. (2010). Semi-supervised novelty detection. Journal of Machine Learning Research, 11, 2973–3009.
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