Infinite hidden relational models

Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Volumn , Issue , 2006, Pages 544-551

  Xu, Zhao ^a   Tresp, Volker ^b   Yu, Kai ^b   Kriegel, Hans Peter ^a  

^a UNIVERSITY OF MUNICH   (Germany)

^b SIEMENS AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; DIRICHLET PROCESS; GENE FUNCTION; GIBBS SAMPLERS; LATENT VARIABLE; MIXTURE MODEL; PROBABILISTIC CONSTRAINTS; RECOMMENDATION ENGINE; RELATIONAL LEARNING; RELATIONAL MODEL; RELATIONAL MODELING; STRUCTURAL LEARNING;

ARTIFICIAL INTELLIGENCE; EQUIVALENCE CLASSES; GENES;

RECOMMENDER SYSTEMS;

EID: 54249116223     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. P. Carbonetto, J. Kisynski, N. de Freitas, and D. Poole (2005). Nonparametric bayesian logic. In Proc. 21th UAI.
2. J. Cheng, C. Hatzis, H. Hayashi, M. Krogel, S. Morishita, D. Page, and J. Sese (2002). KDD Cup 2001 report. SIGKDD Explorations, 3(2):47-64.
3. S. Dzeroski, and N. Lavrac, (ed.), (2001). Relational Data Mining. Springer.
4. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer (1999). Learning probabilistic relational models. Proc. 16th IJ-CAI, 1300-1309. Morgan Kaufmann.
5. L. Getoor, N. Friedman, D. Koller, and B. Taskar (2003). Learning probabilistic models of link structure. Journal of Machine Learning Research, 3:679-707.
6. L. Getoor, D. Koller, and N. Friedman (2000). From instances to classes in probabilistic relational models. In Proc. ICML 2000 Workshop on Attribute-Value and Relational Learning: Crossing the Boundarie. Stanford, CA.
7. D. Heckerman, C. Meek, and D. Koller (2004). Probabilistic Models for Relational Data (Technical Report MSR-TR-2004-30). Microsoft.
8. C. Kemp, T. Griffiths, and J. R. Tenenbaum (2004). Discovering Latent Classes in Relational Data (Technical Report AI Memo 2004-019).
9. L. D. Raedt, and K. Kersting (2003). Probabilistic logic learning. SIGKDD Explor. Newsl., 5:31-48.
10. M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth (2004). The author-topic model for authors and documents. In Proc. 20th UAI, 487-494. AUAI Press.
11. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl (2000). Analysis of recommender algorithms for e-commerce. In Proc. ACM E-Commerce Conference, 158-167. ACM Press.
12. Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei (2004). Hierarchical Dirichlet Processes (Technical Report 653). UC Berkeley Statistics.
13. V. Tresp, and K. Yu (2004). An introduction to non-parametric hierarchical Bayesian modelling. In Proc. the Hamilton Summer School on Switching and Learning in Feedback Systems, 290-312. Springer.
14. V. Tresp (2006). Dirichlet processes and nonparametric Bayesian modelling. Online tutorial.
15. S. Wrobel (2001). Inductive logic programming for knowledge discovery in databases. In S. Dzeroski and N. Lavrac (ed.), Relational Data Mining, 74-101. Springer.
16. Z. Xu, V. Tresp, K. Yu, S. Yu, and H.-P. Kriegel (2005). Dirichlet enhanced relational learning. In Proc. 22nd ICML, 1004-1011. ACM Press.
17. J. Yedidia, W. Freeman, and Y. Weiss (2005). Constructing free-energy approximations and generalized belief propagation algorithms. IEEE Transactions on Information Theory, 51(7):2282-2312. (Pubitemid 41136394)
18. K. Yu, V. Tresp, and S. Yu (2004). A nonparametric hierarchical bayesian framework for information filtering. In Proc. 27th SIGIR, 353-360. ACM Press.