References

The following list gives some literature references related to Probabilistic-Logical Models.
[1] E. Altendorf and B. D'Ambrosio. Feature Definition and Discovery in Probabilistic Relational Models. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 11-16, Banff, Canada, July 2004.
[ bib ]
[2] C. R. Anderson, P. Domingos, and D. S. Weld. Relational Markov Models and their Application to Adaptive Web Navigation. In D. Hand, D. Keim, O. R. Zaďne, and R. Goebel, editors, Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining (KDD-02), pages 143-152, Edmonton, Canada, July 2002. ACM Press.
[ bib ]
[3] N. Angelopoulos and J. Cussens. Markov chain Monte Carlo using tree-based priors on model structure. In J. Breese and D. Koller, editors, Proceedings of the Seventeenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-01), pages 16-23, Seattle, Washington, USA, 2001. Morgan Kaufmann.
[ bib ]
[4] A. Atamentov and V. Honavar. Speeding up multi-relational data mining. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 1-7, Acapulco, Mexico, August 11, 2003.
[ bib ]
[5] F. Bacchus. Using first-order probability logic for the construction of bayesian networks. In D. Heckerman and A. Mamdani, editors, Proceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence (UAI-93), pages 219-226, Providence, Washington, DC, USA, 1993. Morgan Kaufmann.
[ bib ]
[6] O. Bangsø, H. Langseth, and T. D. Nielsen. Structural learning in object oriented domains. In I. Russell and J. Kolen, editors, Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS-01), pages 340-344, Key West, Florida, USA, 2001. AAAI Press.
[ bib ]
[7] D. Barbara. A Probabilistic Relational Data Model. In F. Bancilhom, C. Thamos, and D. Tsichritzis, editors, Advances in Database Technology, International conference on extending database technology, LNCS, pages 60-74. Springer, March 1990.
[ bib ]
[8] F. Bergadano and D. Gunetti. Inductive Logic Programming: From Machine Learning to Software Engeneering. MIT Press, 1996.
[ bib ]
[9] A. Bernstein, S. Clearwater, and F. Provost. The relational vector-space model and industry classification. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 8-18, Acapulco, Mexico, August 11, 2003.
[ bib ]
[10] M. Bilenko and S. Basu. A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Marko Random Fields. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 17-22, Banff, Canada, July 2004.
[ bib ]
[11] A. Blau and A. McGovern. Categorizing unsupervised relational learning algorithms. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 19-21, Acapulco, Mexico, August 11, 2003.
[ bib ]
[12] H. Blockeel and M. Bruynooghe. Aggregation version selection bias, and relational neural networks. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 22-23, Acapulco, Mexico, August 11, 2003.
[ bib ]
[13] H. Blockeel and W. Uwents. Using neural networks for relational learning. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 23-28, Banff, Canada, July 2004.
[ bib ]
[14] M. Boutell and C. Brown. Learning Spatial Configuration Models Using Modified Dirichlet Prior. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 29-34, Banff, Canada, July 2004.
[ bib ]
[15] I. Bratko. PROLOG: Programming for Artificial Intelligence. Addison-Wesley, 1986.
[ bib ]
[16] J. S. Breese. Construction of Belief and decision networks. Computational Intelligence, 8(4):624-647, 1992.
[ bib ]
[17] J. S. Breese, R. P. Goldman, and M. P. Wellman. Introduction to the special section on knowledge-based construction of probabilistic and decision models. Cybernetics, 24(11):1577-1579, 1994.
[ bib ]
[18] R. C. Bunescu and R. J. Mooney. Relational Markov Networks for Collective Information Extraction. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 35-41, Banff, Canada, July 2004.
[ bib ]
[19] W. Buntine. A guide to the literature on learning probabilistic networks from data. IEEE Transaction on Knowledge and Data Engineering, 8:195 - 210, 1996.
[ bib ]
[20] L. Burnell and E. Horvitz. Structure and chance: Melding logic and probability for software debugging. Communications of the ACM, 38(3):31-41, March 1995.
[ bib ]
[21] P. P. S. Chen. The Entity Relationship model: Toward a unified view of data. ACM Transaction on Database Systems, 1(1):9-36, 1976.
[ bib ]
[22] D. M. Chickering, D. Geiger, and D. Heckerman. Learning Bayesian networks is np-hard. Technical report, Microsoft Research, One Microsoft Way, Redmond, WA 98052, November 1994.
[ bib ]
[23] G. F. Cooper. The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence, 42:393-405, 1990.
[ bib ]
[24] R. G. Cowell, A. P. Dawid, S. L. Lauritzen, and D. J. Spiegelhalter. Probabilistic networks and expert systems. Statistics for engineering and information. Springer-Verlag, 1999.
[ bib ]
[25] M. Craven and S. Slattery. Relational Learning with Statistical Predicate Invention: Better Models for Hypertext. Machine Learning, 43(1-2):97-119, 2001.
[ bib ]
[26] C. Cumby and D. Roth. Feature extraction languages for propositionalzed relational learning. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 24-31, Acapulco, Mexico, August 11, 2003.
[ bib ]
[27] J. Cussens. Integrating probabilistic and logical reasoning. In Electronic Transaction on Artificial Intelligence, 1999. Machine Intelligence Workshop (MI16), special issue, (submitted).
[ bib ]
[28] J. Cussens. Loglinear models for first-order probabilistic reasoning. In K. B. Laskey and H. Prade, editors, Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-99), pages 126-133, Stockholm, Sweden, 1999. Morgan Kaufmann.
[ bib ]
[29] J. Cussens. Stochastic logic programs: Sampling, inference and applications. In C. Boutilier and M. Goldszmidt, editors, Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-00), pages 115-122, Stanford, CA,, USA, 2000. Morgan Kaufmann.
[ bib ]
[30] J. Cussens. Parameter estimation in stochastic logic programs. Machine Learning, 44(3):245-271, 2001.
[ bib ]
[31] J. Cussens. Statistical aspects of stochastic logic programs. In T. Jaakkola and T. Richardson, editors, Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics 2001, pages 181-186, Key West, Florida, USA, 2001. Morgan Kaufmann.
[ bib ]
[32] J. Cussens. Individuals, relations and structures in probabilistic models. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 32-36, Acapulco, Mexico, August 11, 2003.
[ bib ]
[33] P. Dagum and M. Luby. Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artificial Intelligence, 60:141-153, 1993.
[ bib ]
[34] B. D'Ambrosio, E. Altendorf, and J. Jorgensen. Ecosystem analysis using probabilistic relational modelling. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 37-42, Acapulco, Mexico, August 11, 2003.
[ bib ]
[35] A. Dayanik and C. G. Nevill-Manning. Clustering in Relational Biological Data. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 42-47, Banff, Canada, July 2004.
[ bib ]
[36] L. De Raedt. Logical settings for concept-learning. Artificial Intelligence, 95(1):197-201, 1997.
[ bib ]
[37] L. De Raedt and M. Bruynooghe. A theory of clausal discovery. In Ruzena Bajcsy, editor, Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), pages 1058-1063, Chambery, France, August 28 - September 3 1993. Morgan Kaufmann.
[ bib ]
[38] L. De Raedt and L. Dehaspe. Clausal discovery. Machine Learning, 26(2-3):99-146, 1997.
[ bib ]
[39] T. Dean and K. Kanazawa. Probabilistic temporal reasoning. In T. M. Mitchell and R. G. Smith, editors, Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI-88), pages 524-528, St. Paul, MN, USA, 1988. AAAI Press / The MIT Press.
[ bib ]
[40] A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc., B 39:1-39, 1977.
[ bib ]
[41] M. Diligenti, P. Frasconi, and M. Gori. Hidden Tree Markov Models for Document Image Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(4):519-523, 2003.
[ bib ]
[42] A. Doan, P. Domingos, and A. Y. Levy. Learning Mappings between Data Schemas. In L. Getoor and D. Jensen, editors, Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL-2000), Technical Report WS-00-06, pages 1-6, Austin/Texas, USA, July 31, 2000. AAAI Press.
[ bib ]
[43] P. Domingos, Y. Abe, C. Anderson, A. Doan, D. Fox, A. Halevy, G. Hulten, H. Kautz, T. Lau, L. Liao, J. Madhavan, Mausum, D. J. Patternson, M. Richardson, S. Sanghai, D. Weld, and S. Wolfman. Research on statistical relational learning at the University of Washington. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 43-47, Acapulco, Mexico, August 11, 2003.
[ bib ]
[44] P. Domingos and M. Richardson. Markov Logic: A Unifying Framework for Statistical Relational Learning. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 48-55, Banff, Canada, July 2004.
[ bib ]
[45] R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press, 1998.
[ bib ]
[46] S. Dzeroski, L. De Raedt, and K. Driessens. Relational reinforcement learning. Machine Learning, 43(1/2):7-52, 2001.
[ bib ]
[47] S. Dzeroski and N. Lavrac. Relational Data Mining. Springer-Verlag, 2001.
[ bib ]
[48] A. Eisele. Towards probabilistic extensions of contraint-based grammars. In J. Dörne, editor, Computational Aspects of Constraint-Based Linguistics Decription-II. DYNA-2 deliverable R1.2.B, 1994.
[ bib ]
[49] I. Fabian and D. A. Lambert. First-order Bayesian reasoning. In G. Antoniou and S. Slaney, editors, Proceedings of 11th Australian Joint Conference on Artificial Intelligence, volume 1502 of LNAI. Springer, 1998.
[ bib ]
[50] W. Feller. An Introduction to Probability Theory and its Applications: Volume 1. Wiley Series in Probability and Mathematical Statistics. John Wiley & Sons, 3rd edition, 1968.
[ bib ]
[51] L. Firoiu. Rule Induction from Noisy Examples. In L. Getoor and D. Jensen, editors, Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL-2000), Technical Report WS-00-06, pages 7-12, Austin/Texas, USA, July 31, 2000. AAAI Press.
[ bib ]
[52] P. Flach. Simply logical: intelligent reasoning by example. John Wiley and Sons Ltd., 1994.
[ bib ]
[53] P. Flach and N. Lachiche. 1BC: A first-order Bayesian classifier. In S. Dzeroski and P. Flach, editors, Proceedings of the Ninth International Workshop on Inductive Logic Programming (ILP-99), volume 1634 of LNAI, pages 92-103, Bled, Slovenia, 1999. Springer.
[ bib ]
[54] P. Frasconi, M. Gori, and A. Sperduti. A general framework for adaptive processing of data structures. IEEE Transactions on Neural Networks, 9(5):768-786, 1998.
[ bib ]
[55] N. Friedman. The Bayesian Structural EM Algorithm. In G. F. Cooper and S. Moral, editors, Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-98), pages 129-138, Madison, Wisconsin, USA, 1998. Morgan Kaufmann.
[ bib ]
[56] N. Friedman, D. Geiger, and M. Goldszmidt. Bayesian network classifiers. Machine Learning, 29:131-163, 1997.
[ bib ]
[57] N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In T. Dean, editor, Proceedings of the Sixteenth International Joint Conferences on Artificial Intelligence (IJCAI-99), pages 1300-1309, Stockholm, Sweden, 1999. Morgan Kaufmann.
[ bib ]
[58] L. Getoor. Learning Statistical Models from Relational Data. PhD thesis, Stanford University, 2001.
[ bib ]
[59] L. Getoor, N. Friedman, and D. Koller. Learning Structured Statistical Models from Relational Data. Linköping Electronic Articles in Computer and Information Science, 7(13), 2002.
[ bib ]
[60] L. Getoor, N. Friedman, D. Koller, and A. Pfeffer. Learning Probabilistic Relational Models. In S. Dzeroski and N. Lavrac, editors, Relational Data Mining. Springer-Verlag, 2001.
[ bib ]
[61] L. Getoor, N. Friedman, D. Koller, and B. Taskar. Learning Probabilistic Models of Relational Structure. In C. E. Brodley and A. Pohoreckyj Danyluk, editors, Proceedings of the Eighteenth International Conference on Machine Learning (ICML-01), pages 170-177, Williamstown, MA, USA, 2001. Morgan Kaufmann.
[ bib ]
[62] L. Getoor, D. Koller, and N. Friedman. From Instances to Classes in Probabilistic Relational Models. In L. De Raedt and S. Kramer, editors, Proceedings of the ICML-2000 Workshop on Attribute-Value and Relational Learning: Crossing the Boundarie, pages 25-34, Stanford, CA, USA, 2000.
[ bib ]
[63] L. Getoor, D. Koller, and B. Taskar. Statistical Models for Relational Data. In S. Dzeroski and L. De Raedt, editors, Workshop Notes of the KDD-02 Workshop on Multi-Relational Data Mining (MRDM-02), pages 36-55, 2002.
[ bib ]
[64] L. Getoor, D. Koller, B. Taskar, and N. Friedman. Learning Probabilistic Relational Models with Structural Uncertainty. In L. Getoor and D. Jensen, editors, Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL-2000), Technical Report WS-00-06, pages 13-20, Austin/Texas, USA, July 31, 2000. AAAI Press.
[ bib ]
[65] L. Getoor, E. Segal, B. Taskar, and D. Koller. Probabilistic Models of Text and Link Structure for Hypertext Classification. In Workshop Notes of IJCAI-01 Workshop on `Text Learning: Beyond Supervision', Washington, USA, 2001.
[ bib ]
[66] L. Getoor, B. Taskar, and D. Koller. Selectivity Estimation using Probabilistic Relational Models. In W. G. Aref, editor, Proceedings of the ACM Special Interest Group on Management of Data Conference (SIGMOD-01), Santa Barbara, CA, USA, 2001.
[ bib ]
[67] W. R. Gilks, A. Thomas, and D. J. Spiegelhalter. A language and program for complex bayesian modelling. The Statistician, 43, 1994.
[ bib ]
[68] S. Glesner and D. Koller. Constructing Flexible Dynamic Belief Networks from First-Order Probabilistic Knowledge Bases. In Ch. Froidevaux and J. Kohlas, editors, Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU-95), volume 946 of LNCS, pages 217 - 226, Fribourg, Switzerland, 1995. Springer-Verlag.
[ bib ]
[69] R. P. Goldman and E. Charniak. Dynamic construction of belief networks. In P. P. Bonissone, M. Henrion, L. N. Kanal, and J. F. Lemmer, editors, Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence (UAI-90), pages 171-184, Cambridge, MA, USA, 1990. Elsevier.
[ bib ]
[70] J. Gonzales, I. Jonyer, L. B. Holder, and D. J. Cook. Efficient Mining of Graph-Based Data. In L. Getoor and D. Jensen, editors, Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL-2000), Technical Report WS-00-06, pages 21-28, Austin/Texas, USA, July 31, 2000. AAAI Press.
[ bib ]
[71] P. Haddawy. Generating Bayesian networks from probabilistic logic knowledge bases. In R. López de Mántaras and D. Poole, editors, Proceedings of the Tenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-1994), pages 262-269, Seattle, Washington, USA, 1994. Morgan Kaufmann.
[ bib ]
[72] P. Haddawy. An Overview of Some Recent Developments on Bayesian Problem-Solving Techniques. AI Magazine - Special Issue on Uncertainty in AI, 20(2):11-29, 1999.
[ bib ]
[73] P. Haddawy, J. W. Helwig, L. Ngo, and R. A. Krieger. Clinical Simulation using Context­-Sensitive Temporal Probability Models. In Proceedings of the Nineteenth Annual Symposium on Computer Applications in Medical Care (SCAMC-95), 1995.
[ bib ]
[74] J. Y. Halpern. An analysis of first-order logics of probability. Artificial Intelligence, 46:311-350, 1989.
[ bib ]
[75] J. Y Halpern. A Logical Approach to Reasoning about Uncertainty: A Tutorial. In X. Arrazola, K. Korta, and F. J. Pelletier, editors, Discourse, Interaction, and Communication. Kluwer, 1997.
[ bib ]
[76] D. Heckerman. A Tutorial on Learning with Bayesian Networks. Technical Report MSR-TR-95-06, Microsoft Research,, One Microsoft Way, Redmond, WA 98052, March 1995.
[ bib ]
[77] D. Heckerman, C. Meek, and D. Koller. Probabilistic Entity-Relationship Models, PRMs, and Plate Models. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 55-60, Banff, Canada, July 2004.
[ bib ]
[78] S. Hill. Social network relational vectors for anonymous identity matching. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 48-52, Acapulco, Mexico, August 11, 2003.
[ bib ]
[79] W. H. Hsu and R. Joehanes. Relational Decision Networks. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 61-67, Banff, Canada, July 2004.
[ bib ]
[80] G. Hulten, P. Domingos, and Y. Abe. Mining massive relational databases. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 53-60, Acapulco, Mexico, August 11, 2003.
[ bib ]
[81] M. Jaeger. Relational Bayesian networks. In D. Geiger and P. P. Shenoy, editors, Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), pages 266-273, Providence, Rhode Island, USA, 1997. Morgan Kaufmann.
[ bib ]
[82] M. Jaeger. Convergence results for relational Bayesian networks. In V. Pratt, editor, Proceedings of IEEE Symposium on Logic in Computer Science (LICS-1998), Indianapolis, Indiana, USA, 1998.
[ bib ]
[83] M. Jaeger. Reasoning about infinite random structures with relational Bayesian networks. In A. G. Cohn, L. Schubert, and S. C. Shapiro, editors, Proceedings of International Conference on the Principles of Knowledge Representation (KR-1998), 1998.
[ bib ]
[84] D. Jensen and J. Neville. Linkage and autocorrelation cause feature selection bias in relational learning. In C. Sammut and A. G. Hoffmann, editors, Proceedings of the Nineteenth International Conference on Machine Learning (ICML-02), pages 259-266, Sydney, Australia, 2002. Morgan Kaufmann.
[ bib ]
[85] F. V. Jensen. An introduction to Bayesian Networks. UCL Press Limited, Gunpowder Square, London EC4A 3DE, 1996. Reprinted 1998.
[ bib ]
[86] F. V. Jensen. Bayesian networks and decision graphs. Springer-Verlag New, 2001.
[ bib ]
[87] M. I. Jordan, editor. Learning in Graphical Models. Kluwer Academic Publishers. Reprinted by MIT Press, 1998.
[ bib ]
[88] Y. Kameya and T. Sato. Efficient EM learning with tabulation for parameterized logic programs. In J. Lloyd, V. Dahl, U. Furbach, M. Kerber, K.-K. Lau, C. Palamidessi, L. M. Pereira, Y. Sagiv, and P. J. Stuckey, editors, Proceedings of the First International Conference on Computational Logic (CL-00), volume 1861 of LNAI, pages 269-294. Springer-Verlag, 2000.
[ bib ]
[89] Y. Kameya, N. Ueda, and T. Sato. A graphical method for parameter learning of symbolic-statistical models. In S. Arikawa and K. Furukawa, editors, Proceedings of the Second International Conference on Discovery Science (DS-99), volume 1721 of LNAI, pages 254-276, Kanagawa, Japan, 1999. Springer-Verlag.
[ bib ]
[90] K. Kersting. Bayessche-logische Programme. Master's thesis, Albert-Ludwigs-University, Freiburg, Germany, April 2000. in German.
[ bib ]
[91] K. Kersting. Representational power of probabilistic-logical models: From upgrading to downgrading. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 61-62, Acapulco, Mexico, August 11, 2003.
[ bib ]
[92] K. Kersting and L. De Raedt. Bayesian logic programs. In J. Cussens and A. Frisch, editors, Work-in-Progress Reports of the Tenth International Conference on Inductive Logic Programming (ILP -2000), 2000. http://SunSITE.Informatik.RWTH-Aachen.DE/ Publications/CEUR-WS/Vol-35/.
[ bib ]
[93] K. Kersting and L. De Raedt. Bayesian Logic Programs. In F. Furukawa, S. H. Muggleton, D. Michie, and L. De Raedt, editors, Proceedings of the Seventeenth Machine Intelligence Workshop, 2000.
[ bib ]
[94] K. Kersting and L. De Raedt. Adaptive Bayesian Logic Programs. In C. Rouveirol and M. Sebag, editors, Proceedings of the Eleventh Conference on Inductive Logic Programming (ILP-01), volume 2157 of LNCS, Strasbourg, France, 2001. Springer.
[ bib ]
[95] K. Kersting and L. De Raedt. Bayesian Logic Programs. Technical Report 151, University of Freiburg, Institute for Computer Science, April 2001.
[ bib ]
[96] K. Kersting and L. De Raedt. Towards Combining Inductive Logic Programming and Bayesian Networks. In C. Rouveirol and M. Sebag, editors, Proceedings of the Eleventh Conference on Inductive Logic Programming (ILP-01), volume 2157 of LNCS, Strasbourg, France, 2001. Springer.
[ bib ]
[97] K. Kersting and L. De Raedt. Principles of Learning Bayesian Logic Programs. Technical Report 174, University of Freiburg, Institute for Computer Science, June 2002.
[ bib ]
[98] K. Kersting, L. De Raedt, and S. Kramer. Interpreting Bayesian Logic Programs. In L. Getoor and D. Jensen, editors, Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL-2000), Technical Report WS-00-06, Austin, Texas, 2000. AAAI Press.
[ bib ]
[99] K. Kersting and L. De Readt. Logical markov decision programs. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 63-70, Acapulco, Mexico, August 11, 2003.
[ bib ]
[100] K. Kersting, T. Raiko, and L. De Raedt. A Structural GEM for Learning Logical Hidden Markov Models. In S. Dzeroski, L. De Raedt, and S. Wrobel, editors, Workshop Notes of the KDD-03 Workshop on Multi-Relational Data Mining (MRDM-03), 2003.
[ bib ]
[101] K. Kersting, T. Raiko, S. Kramer, and L. De Raedt. Towards discovering structural signatures of protein folds based on logical hidden markov models. In R. B. Altman, A. K. Dunker, L. Hunter, T. A. Jung, and T. E. Klein, editors, Proceedings of the Pacific Symposium on Biocomputing, pages 192 - 203, Kauai, Hawaii, USA, 2003. World Scientific.
[ bib ]
[102] D. Koller. Probabilistic relational models. In S. Dzeroski and P. Flach, editors, Proceedings of Ninth International Workshop on Inductive Logic Programming (ILP-99), volume 1634 of LNAI, pages 3-13, Bled, Slovenia, June 1999. Springer.
[ bib ]
[103] D. Koller, A. Levy, and A. Pfeffer. P-classic: A tractable probabilistic description logic. In B. J. Kuipers and B. Webber, editors, Proceedings of the Fourteenth National Conference on AI (AAAI-97), pages 390-397, Providence, Rhode Island, August 1997.
[ bib ]
[104] D. Koller, D. McAllester, and A. Pfeffer. Effective Bayesian Inference for Stochastic Programs. In B. J. Kuipers and B. Webber, editors, Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-1997), pages 740-747, 1997.
[ bib ]
[105] D. Koller and A. Pfeffer. Learning probabilities for noisy first-order rules. In M. P. Georgeff and M. E. Pollack, editors, Proceedings of the Fifteenth Joint Conference on Artificial Intelligence (IJCAI-97), pages 1316-1321, Nagoya, Japan, August 1997.
[ bib ]
[106] D. Koller and A. Pfeffer. Object-oriented Bayesian networks. In D. Geiger and P. P. Shenoy, editors, Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), pages 302-313, Providence, Rhode Island, USA, 1997. Morgan Kaufmann.
[ bib ]
[107] D. Koller and A. Pfeffer. Probabilistic frame-based systems. In C. Rich and J. Mostow, editors, Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 580-587, Madison, Wisconsin, USA, July 1998. AAAI Press.
[ bib ]
[108] N. Lachiche and P. Flach. 1BC2: A True First-Order Bayesian Classifier. In S. Matwin and C. Sammut, editors, Proceedings of the Twelfth International Conference on Inductive Logic Prgramming (ILP-02), volume 2583 of LNCS, pages 133-148, Sydney, Australia, 2002. Springer.
[ bib ]
[109] H. Langseth and O. Bangsø. Parameter learning in object oriented Bayesian networks. Annals of Mathematics and Artificial Intelligence, 32(1/2):221-243, 2001.
[ bib ]
[110] S. L. Lauritzen. The EM algorithm for graphical association models with missing data. Computational Statistics and Data Analysis, 19:191-201, 1995.
[ bib ]
[111] J. W. Lloyd. Foundations of Logic Programming. Springer, Berlin, 2. edition, 1989.
[ bib ]
[112] C. H. Manning and H. Schütze. Foundations of Statistical Natural Language Processing. The MIT Press, 1999.
[ bib ]
[113] B. Marthi, B. Milch, and S. Russell. First-order probabilistic models for information extraction. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 71-78, Acapulco, Mexico, August 11, 2003.
[ bib ]
[114] A. McCallum and D. Jensen. A Note on the Uinification of Information Extraction and Data Mining using Conditional-Probability, Relational Models. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 79-86, Acapulco, Mexico, August 11, 2003.
[ bib ]
[115] G. J. McKachlan and T. Krishnan. The EM Algorithm and Extensions. John Eiley & Sons, Inc., 1997.
[ bib ]
[116] B. Milch, B. marthi, and S. Russell. BLOG: Relational Modeling with Unkown Objects. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 67-73, Banff, Canada, July 2004.
[ bib ]
[117] R. Milton, V. Maheswari, and A. Siromoney. T he Variable Precision Rough Set Inductive Logic Programming modell - a statistical relational prespective. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 87-92, Acapulco, Mexico, August 11, 2003.
[ bib ]
[118] T. M. Mitchell. Machine Learning. The McGraw-Hill Companies, Inc., 1997.
[ bib ]
[119] S. H. Muggleton. Stochastic logic programs. In L. De Raedt, editor, Advances in Inductive Logic Programming. IOS Press, 1996.
[ bib ]
[120] S. H. Muggleton. Learning stochastic logic programs. Electronic Transactions in Artificial Intelligence, 4(041), 2000.
[ bib ]
[121] S. H. Muggleton. Learning stochastic logic programs. In L. Getoor and D. Jensen, editors, Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL-2000), Technical Report WS-00-06, pages 36 -41, Austin, Texas, 2000. AAAI Press.
[ bib ]
[122] S. H. Muggleton. Semantics and derivation for stochastic logic programs. In R. Dybowski, J. Myers, and S. Parsons, editors, Workshop Notes of UAI-00 Workshop on Fusion of Domain Knowledge with Data for Decision Support, Stanford, CA, USA, 2000.
[ bib ]
[123] S. H. Muggleton. Learning structure and parameters of stochastic logic programs. In S. Matwin and C. Sammut, editors, Proceedings of the Twelfth International Conference on Inductive Logic Prgramming (ILP-02), volume 2583 of LNCS, pages 198-206, Sydney, Australia, 2002. Springer.
[ bib ]
[124] S. H. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19(20):629-679, 1994.
[ bib ]
[125] K. P. Murphy. Bayes Net Toolbox for Matlab. U. C. Berkeley. http://www.ai.mit.edu/\ murphyk/Software/BNT/bnt.html.
[ bib ]
[126] C. Nédellec, C. Rouveirol, H. Adé, F. Bergadano, and B. Tausend. Declarative Bias in ILP. In L. De Raedt, editor, Advances in Inductive Logic Programming. IOS Press, 1996.
[ bib ]
[127] J. Neville and D. Jensen. Iterative Classification in Relational Data. In L. Getoor and D. Jensen, editors, Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL-2000), Technical Report WS-00-06, pages 42-49, Austin/Texas, USA, July 31, 2000. AAAI Press.
[ bib ]
[128] J. Neville, M. Rattigan, and D. Jensen. Statistical relational learning: Four claims and a survey. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 93-97, Acapulco, Mexico, August 11, 2003.
[ bib ]
[129] J. Neville, O. Simsek, and D. Jensen. Autocorreltion and Relational Learning: Challenges and Opportunities. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 74-81, Banff, Canada, July 2004.
[ bib ]
[130] J. Newton and R. Greiner. Hierarchical Probabilistic Relationals Models for Collaborative Filtering. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 82-88, Banff, Canada, July 2004.
[ bib ]
[131] R. Ng and V. S. Subrahmanian. Probabilistic Logic Programming. Information and Computation, 101(2):150-201, 1992.
[ bib ]
[132] L. Ngo and P. Haddawy. Probabilistic logic programming and Bayesian networks. In S. Goto, J. Jaffar, and K. Kanchanasut, editors, Algorithms, Concurrency and Knowledge: Proceedings of the Asian Computing Science Conference 1995, Pathumthai, Thailand, December 1995.
[ bib ]
[133] L. Ngo and P. Haddawy. A Knowledge-Based Model Construction Approach to Medical Decision Making. In Proceedings of the Twentieth American Medical Informatics Association Annual Fall Symposium (AMIA-96), Washington DC, USA, 1996.
[ bib ]
[134] L. Ngo and P. Haddawy. Answering Queries from Context-Sensitive Probabilistic Knowledge Bases. Theoretical Computer Science, 171:147-177, 1997.
[ bib ]
[135] L. Ngo, P. Haddawy, and J. Helwig. A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection. In P. Besnard and S. Hanks, editors, Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence (UAI-1995), Montreal, Quebec, August 18-20, 1995.
[ bib ]
[136] S.-H. Nienhuys-Cheng and R. de Wolf. Foundations of Inductive Logic Programming. Springer-Verlag, 1997.
[ bib ]
[137] T. Oates, F. Huang, and S. Doshi. Parameter estimation for stochastic context-free graph grammars. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 98-106, Acapulco, Mexico, August 11, 2003.
[ bib ]
[138] R. Páircéir, S. McClean, and B. Scotney. Uing Hierachies, Aggregates and Statistical Model to Discover Knowledge from Distributed Databases. In L. Getoor and D. Jensen, editors, Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL-2000), Technical Report WS-00-06, pages 50-56, Austin/Texas, USA, July 31, 2000. AAAI Press.
[ bib ]
[139] S. Parsons. Current approaches to handling imperfect information in data and knowledge bases. IEEE Transactions on Knowledge and Data Engineering, 8(3):353-372, June 1996.
[ bib ]
[140] H. Pasula, B. Marthi, B. Milch, S. Russell, and I. Shpitser. Identity Uncertainty and Citation Matching. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15. MIT Press, 2003.
[ bib ]
[141] H. Pasula and S. Russell. Approximate inference for first-order probabilistic languages. In B. Nebel, editor, Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pages 741-748, Seattle, Washington, USA, 2001. Morgan Kaufmann.
[ bib ]
[142] J. Pearl. Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 2. edition, 1991.
[ bib ]
[143] C. Perlich and F. Provost. Aggregation and concept complexity in relational learning. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 107-108, Acapulco, Mexico, August 11, 2003.
[ bib ]
[144] A. Pfeffer. A Bayesian Language for Cumulative Learning. In L. Getoor and D. Jensen, editors, Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL-2000), Technical Report WS-00-06, pages 57-62, Austin/Texas, USA, July 31, 2000. AAAI Press.
[ bib ]
[145] A. Pfeffer and D. Koller. Semantics and Inference for Recursive Probability Models. In H. Kautz and B. Porter, editors, Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-00), pages 538-544., Austin, Texas, USA, 2000. AAAI Press.
[ bib ]
[146] A. Pfeffer, D. Koller, B. Milch, and K. T. Takusagawa. Spook: A system for probabilistic object-oriented knowledge representation. In K. B. Laskey and H. Prade, editors, Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-1999). Morgan Kaufmann, July 30-August 1, 1999.
[ bib ]
[147] A. J. Pfeffer. Probabilistic Reasoning for Complex Systems. PhD thesis, Stanford University, 1999.
[ bib ]
[148] A. J. Pfeffer. Probabilistic Reasoning for Complex Systems. PhD thesis, Stanford University, 2000.
[ bib ]
[149] D. Poole. Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence, 64:81-129, 1993.
[ bib ]
[150] A. Popescul and L. H. Ungar. Statistical relational learning for link prediction. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 109-115, Acapulco, Mexico, August 11, 2003.
[ bib ]
[151] A. Popescul and L. H. Ungar. Cluster-based Concept Invention for Statistical Relational Learning. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 102-109, Banff, Canada, July 2004.
[ bib ]
[152] F. Provost, C. Perlich, and S .Macskassy. Relational learning problems and simple models. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 116-120, Acapulco, Mexico, August 11, 2003.
[ bib ]
[153] A. Puech and S. Muggleton. A comparison of Stochastic Logic Programs and Baysian Logic Programs. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 121-129, Acapulco, Mexico, August 11, 2003.
[ bib ]
[154] M. L. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, Inc., 1994.
[ bib ]
[155] L. R. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77(2):257-286, February 1989.
[ bib ]
[156] L. R. Rabiner and B. H. Juang. An introduction to hidden Markov models. IEEE ASSP Magazine, pages 4-15, January 1986.
[ bib ]
[157] L. De Raedt. Attribute-value learning versus inductive logic programming: the missing links. In Inductive Logic Programming, 1999.
[ bib ]
[158] F. T. Ramos and H. F. Durrant-Whyte. Learning Complex Motion Structures. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 88-83, Banff, Canada, July 2004.
[ bib ]
[159] V. Santos Costa, D. Page, M. Qazi, and J. Cussens. CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. In Proceedings of the Nineteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-03), Mexico, August 8-10 2003. Morgan Kaufman.
[ bib ]
[160] T. Sato. A Statistical Learning Method for Logic Programs with Distribution Semantics. In L. Sterling, editor, Proceedings of the Twelfth International Conference on Logic Programming (ICLP-1995), pages 715 - 729, Tokyo, Japan, 1995. MIT Press.
[ bib ]
[161] T. Sato. Parameterized logic programs where computing meets learning. In H. Kuchen and K. Ueda, editors, Proceedings of Fifth International Symposium on Functional and Logic Programming (FLOPS-01), volume 2024 of LNCS, pages 40-60, Tokyo, Japan, 2001. Springer-Verlag.
[ bib ]
[162] T. Sato and Y. Kameya. PRISM: A Symbolic-Statistical Modeling Language. In M. P. Georgeff and M. E. Pollack, editors, Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pages 1330-1339, Nagoya, Japan, 1997. Morgan Kaufmann.
[ bib ]
[163] T. Sato and Y. Kameya. A Viterbi-like algorithm and EM learning for statistical abduction. In R. Dybowski, J. Myers, and S. Parsons, editors, Workshop Notes of UAI-00 Workshop on Fusion of Domain Knowledge with Data for Decision Support, Stanford, CA, USA, 2000.
[ bib ]
[164] T. Sato and Y. Kameya. Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research, 15:391-454, 2001.
[ bib ]
[165] T. Sato and Y. Kameya. A Dynamic Programming Approach to Parameter Learning of Generative Models with Failure. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 94-101, Banff, Canada, July 2004.
[ bib ]
[166] T. Sato and N.-F. Zhou. A new perspective of statistical modeling with PRISM. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 133-140, Acapulco, Mexico, August 11, 2003.
[ bib ]
[167] E. Segal, Y. Barash, I. Simon, N. Friedman, and D. Koller. From Promoter Sequence to Expression: A Probabilistic Framework. In G. Myers, S. Hannenhalli, S. Istrail, P. Pevzner, and M. Waterman, editors, Proceedings of the Sixth International Conference on Research in Computational Molecular Biology (RECOMB-02), pages 263-272, Washington, DC, USA, 2002. ACM Press.
[ bib ]
[168] E. Segal, A. Battle, and D. Koller. Decomposing Gene Expression into Cellular Processes. In R. B. Altman, A. K. Dunker, L. Hunter, T. A. Jung, and T. E. Klein, editors, Proceedings of the Pacific Symposium on Biocomputing, pages 89 - 100, Kauai, Hawaii, USA, 2003. World Scientific.
[ bib ]
[169] E. Segal, B. Taskar, A. Gasch, N. Friedman, and D. Koller. Rich Probabilistic Models for Gene Expression. Bioinformatics, 17:S243-S252, 2001.
[ bib ]
[170] R. D. Shachter. Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams. In G. F. Cooper and S. Moral, editors, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-1998), pages 480-487, Madison, Wisconsin, USA, 1998.
[ bib ]
[171] S. Shanghai, P. Domingos, and D. Weld. Learning statistical models of time-varying relational data. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 131-133, Acapulco, Mexico, August 11, 2003.
[ bib ]
[172] E. Y. Shapiro. Algorithmic Program Debugging. MIT Press, 1983.
[ bib ]
[173] E. Y. Shapiro. Logic Programs with Uncertainties: A Tool for Implementing Expert Systems. In A. Bundy, editor, Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-1983), pages 529-532, Karlsruhe, Germany, 1983. Kaufmann.
[ bib ]
[174] S. Slattery. Relational learning: A web-page classification viewpoint. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 141-142, Acapulco, Mexico, August 11, 2003.
[ bib ]
[175] P. Smyth. Statistical modeling of graph and network data. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 143-144, Acapulco, Mexico, August 11, 2003.
[ bib ]
[176] B. Taskar, P. Abbeel, M.-F. Wong, and D. Koller. Label and link prediction in relational data. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 145-152, Acapulco, Mexico, August 11, 2003.
[ bib ]
[177] B. Taskar, E. Segal, and D. Koller. Probabilistic clustering in relational data. In B.`Nebel, editor, Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pages 870-87, Seattle, Washington, USA, 2001. Morgan Kaufmann.
[ bib ]
[178] A. Van Assche, C. Vens, H. Blockeel, and S. Dzeroski. A Random Forest Approach to Relational Learning. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 110-116, Banff, Canada, July 2004.
[ bib ]
[179] A. Van Gelder, K. A. Ross, and J. S. Schlipf. The Well-Founded Semantics for general Logic Programs. Journal of the ACM, 38(3):620-650, 1991.
[ bib ]
[180] W. Van Laer and L. De Raedt. How to Upgrade Propositional Learners to First Order Logic: A Case Study. In N. Lavrac and S. Dzeroski, editors, Relational Data Mining. Springer Verlag, 2001.
[ bib ]
[181] A. P. Wolfe and D. Jensen. Playing multiple roles: Discovering Overlapping Roles in Social Networks. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 117-125, Banff, Canada, July 2004.
[ bib ]
[182] Y. Xiang, S. K. Wong, and N. Cercone. Critical remarks on single link search in learning belief networks. In E. Horvitz and F. V. Jensen, editors, Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence (UAI-96), pages 564-571, Portland, Oregon, USA, 1996. Morgan Kaufmann.
[ bib ]
[183] N. L. Zhang and D. Poole. Exploiting causal independence in Bayesian network inference. Journal of Artificial Intelligence Research, 5:301-328, 1996.
[ bib ]
[184] X.-F. Zhang, C. M. Lam, and W. K. Cheung. Web Page Organization and Visualization Using Generative Topographic Mapping: A Pilot Study. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 126-131, Banff, Canada, July 2004.
[ bib ]
[185] D. Zhou and B. Schoelkopf. A Regularization Framework for Learning from Graph Data. In T. Dietterich, L. Getoor, and K. Murphy, editors, Working Notes of the ICML-2004 Workshop on Statistical Relational Learning and Connections to Other Fields (SRL-2004), pages 132-137, Banff, Canada, July 2004.
[ bib ]
[186] N.-F. Zhou, T. Sato, and K. Hasidad. Toward a high-performance system for symbolic and statistical modeling. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-2003), pages 153-159, Acapulco, Mexico, August 11, 2003.
[ bib ]

This file has been generated by bibtex2html 1.54