Structure Learning of Probabilistic Relational Models from Incomplete Relational Data-Reference-Cited by-同舟云学术

Structure Learning of Probabilistic Relational Models from Incomplete Relational Data

Published: Issue: Volume: Page:214-225
ISSN:0302-9743
Container-title:Machine Learning: ECML 2007
language:
Short-container-title:

Author:

Li Xiao-Lin,Zhou Zhi-Hua

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-540-74958-5_22.pdf

Reference23 articles.

1. Anderson, C., Domingos, P., Weld, D.: Relational Markov models and their application to adaptive web navigation. In: KDD 2002, Edmonton, Canada, pp. 143–152 (2002)

2. Cheng, J., Greiner, R., Kelly, J.: Learning Bayesian networks from data: An efficient algorithm based on information theory. Artificial Intelligence 137, 43–90 (2002)

3. Chickering, D.M.: Learning Bayesian networks is NP-complete. In: Fisher, D., Lenz, H.J. (eds.) Learning from Data: Artificial Intelligence and Statistics V, pp. 121–130. Springer, Berlin (1996)

4. Chow, C.K., Liu, C.N.: Approximating discrete probability distributions with dependence trees. IEEE Trans. Information Theory 14, 462–467 (1968)

5. De Raedt, L., Kimmig, A., Toivonen, H.: ProbLog: A probabilistic prolog and its application in link discovery. In: IJCAI 2007, Hyderabad, India, pp. 2462–2467 (2007)

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Learning Probabilistic Relational Models Using an Ontology of Transformation Processes;On the Move to Meaningful Internet Systems. OTM 2017 Conferences;2017

2. A selective neural network ensemble classification for incomplete data;International Journal of Machine Learning and Cybernetics;2016-04-01

3. Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases;Machine Learning;2015-02-20