정보과학회지 (Communications of the Korean Institute of Information Scientists and Engineers)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지

빅데이터를 위한 확률 관계형 학습 모델의 변분 추론

  • 발행 : 2014.07.15
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

키워드

참고문헌

  1. Baabak Ahmadi, Kristian Kersting, and Scott Sanner. Multi-evidence lifted message passing, with application to pagerank and the kalman filter. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAl), pp. 1152 - 1158, 2011.
  2. Fahiem Bacchus. Representing and reasoning with probabilistic knowledge: a logical approach to probabilities. MIT Press, Cambridge, MA, USA, 1990.
  3. Peter Carbonetto, Jacek Kisynski, Nando de Freitas, and David Poole. Nonparametric bayesian logic. In Proceedings of the 21st Conference in Uncertainty in Artificial Intelligence (UAl), pp. 85 - 93, 2005.
  4. Jaesik Choi and Eyal Amir, Lifted Relational Variational Inference, In Proceedings of the Twenty-Eight Conference on Uncertainty in Artificial Intelligence (UAl), pp. 196-206, 2012.
  5. Jaesik Choi, Eyal Amir, and David Hill. Lifted inference for relational continuous models. In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAl), pp. 126 - 134, 2010.
  6. Jaesik Choi, Rodrigo de Salvo Braz, and Hung H. Bui. Efficient methods for lifted inference with aggregate factors. In Proceedings of the Twenty-Fifth AAAl Conference on Artificial Intelligence (AAAI), pp. 1030-1036, 2011.
  7. Jaesik Choi, Abner Guzman-Rivera, and Eyal Amir. Lifted relational kalman filtering. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), pp. 2092 - 2099, 2011.
  8. B. de Finetti. Funzione caratteristica di unfenomeno aleatorio. Mathematice e Naturale, 1931.
  9. Rodrigo de Salvo Braz, Eyal Amir, and Dan Roth. Lifted first-order probabilistic inference. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAl), pp. 1319 - 1325, 2005.
  10. P. Diaconis and D. Freedman. Finite exchangeable sequences. Annals of Probability, 8(1): 115 - 130, 1980. https://doi.org/10.1214/aop/1176994828
  11. Nir Friedman, Lise Getoor, Daphne Koller, and Avi Pfeffer. Learning probabilistic relational models. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAl), pp. 1300-1309, 1999.
  12. Lise Getoor and Ben Taskar, Introduction to Statistical Relational Learning, MlT Press, Cambridge, MA, USA, 2007.
  13. Joseph Y. Halpern. An analysis of first-order logics of probability. Artificial Intelligence, 46(3):311-350, 1990. https://doi.org/10.1016/0004-3702(90)90019-V
  14. E. Hewitt and L.J. Savage. Symmetric measures on cartesian products. Trans. Amer. Math. Soc., 80:470-501, 1955. https://doi.org/10.1090/S0002-9947-1955-0076206-8
  15. Michael I. Jordan, Zoubin Ghahramani, and Tommi Jaakkola, and Lawrence K. Saul. An introduction to variational methods for graphical models. Machine Learning, 37(2): 183-233, 1999. https://doi.org/10.1023/A:1007665907178
  16. Kristian Kersting and Luc De Raedt, Bayesian Logic Programs, Technical report, Albert-Ludwigs University at Freiburg, 2001.
  17. Andrew McCallum, Karl Schultz, and Sameer Singh. FACTORIE: Probabilistic Programming via lmperatively Defined Factor Graphs. In Advances on Neural. Information Processing Systems (NIPS), 2009.
  18. Brian Milch, Bhaskara Marthi, Stuart J. Russell, David Sontag, Daniel L. Ong, and Andrey Kolobov. BLOG: Probabilistic models with unknown objects. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAl), pp. 1352-1359, 2005.
  19. Brian Milch, Luke S. Zettlemoyer, Kristian Kersting, Michael Haimes, and Leslie Pack Kaelbling. Lifted probabilistic inference with counting formulas. In Proceedings of the Twenty-Third AAAl Conference on Artificial Intelligence (AAAl), pp. 1062-1068, 2008.
  20. Galileo Namata, Stanley Kok, and Lise Getoor. Collective Graph Identification. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2011
  21. Raymond Ng and V. S. Subrahmanian. Probabilistic logic programming. Information and Computation, 101(2): 150-201, 1992. https://doi.org/10.1016/0890-5401(92)90061-J
  22. Avi Pfeffer, Daphne Koller, Brian Milch, and Ken T. Takusagawa. Spook: A system for probabilistic object-oriented knowledge representation. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI), pp. 541-550, 1999.
  23. David Poole. First-order probabilistic inference. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAl), pp. 985-991, 2003.
  24. Matthew Richardson and Pedro Domingos. Markov logic networks. Machine Learning, 62(1-2): 107-136, 2006. https://doi.org/10.1007/s10994-006-5833-1
  25. Parag Singla and Pedro Domingos. Lifted firstorder belief propagation. In Proceedings of the Twenty-Third AAAl Conference on Artificial Intelligence (AAAI), pp. 1094-1099, 2008.