Probabilistic Inductive Querying Using ProbLog
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Luc De Raedt | Kristian Kersting | Hannu Toivonen | Vítor Santos Costa | Bernd Gutmann | Angelika Kimmig | V. S. Costa | K. Kersting | L. D. Raedt | Hannu (TT) Toivonen | Angelika Kimmig | Bernd Gutmann
[1] Stephen Muggleton,et al. Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract) , 2007, ILP.
[2] Luc De Raedt,et al. Learning the Parameters of Probabilistic Logic Programs from Interpretations , 2011, ECML/PKDD.
[3] Maurice Bruynooghe,et al. Logic programs with annotated disjunctions , 2004, NMR.
[4] Yoshitaka Kameya,et al. Parameter Learning of Logic Programs for Symbolic-Statistical Modeling , 2001, J. Artif. Intell. Res..
[5] Taisuke Sato,et al. Propositionalizing the EM algorithm by BDDs , 2010 .
[6] Lise Getoor,et al. Learning Probabilistic Relational Models , 1999, IJCAI.
[7] Luc De Raedt,et al. Probabilistic Inductive Logic Programming - Theory and Applications , 2008, Probabilistic Inductive Logic Programming.
[8] Fabrizio Riguzzi,et al. A Top Down Interpreter for LPAD and CP-Logic , 2007, AI*IA.
[9] Randal E. Bryant,et al. Graph-Based Algorithms for Boolean Function Manipulation , 1986, IEEE Transactions on Computers.
[10] Edward Fredkin,et al. Trie memory , 1960, Commun. ACM.
[11] James Cussens,et al. CLP(BN): Constraint Logic Programming for Probabilistic Knowledge , 2002, Probabilistic Inductive Logic Programming.
[12] Luc De Raedt,et al. On the Efficient Execution of ProbLog Programs , 2008, ICLP.
[13] Norbert Fuhr,et al. Probabilistic datalog: Implementing logical information retrieval for advanced applications , 2000, J. Am. Soc. Inf. Sci..
[14] Bart Demoen,et al. A simplified fast interface for the use of CUDD for Binary Decision Diagrams , 2008 .
[15] Luc De Raedt,et al. Parameter Learning in Probabilistic Databases: A Least Squares Approach , 2008, ECML/PKDD.
[16] Raymond J. Mooney,et al. Automated refinement of first-order horn-clause domain theories , 2005, Machine Learning.
[17] Luc De Raedt,et al. On the implementation of the probabilistic logic programming language ProbLog , 2010, Theory and Practice of Logic Programming.
[18] Hannu Toivonen,et al. Link Discovery in Graphs Derived from Biological Databases , 2006, DILS.
[19] Vítor Santos Costa. The Life of a Logic Programming System , 2008, ICLP.
[20] Pat Langley,et al. Unifying Themes in Empirical and Explanation-Based Learning , 1989, ML.
[21] Luc De Raedt,et al. ProbLog: A Probabilistic Prolog and its Application in Link Discovery , 2007, IJCAI.
[22] Haym Hirsh,et al. Explanation-based Generalization in a Logic-Programming Environment , 1987, IJCAI.
[23] Ronen Feldman,et al. Bias-Driven Revision of Logical Domain Theories , 1993, J. Artif. Intell. Res..
[24] Edward Hung,et al. Mining Frequent Itemsets from Uncertain Data , 2007, PAKDD.
[25] Luc De Raedt,et al. Local Query Mining in a Probabilistic Prolog , 2009, IJCAI.
[26] Heikki Mannila,et al. A database perspective on knowledge discovery , 1996, CACM.
[27] Rahul Gupta,et al. Creating probabilistic databases from information extraction models , 2006, VLDB.
[28] Leslie G. Valiant,et al. The Complexity of Enumeration and Reliability Problems , 1979, SIAM J. Comput..
[29] Camilo Rueda,et al. Stochastic Behavior and Explicit Discrete Time in Concurrent Constraint Programming , 2008, ICLP.
[30] Luc De Raedt,et al. Probabilistic Rule Learning , 2010, ILP.
[31] S. Muggleton. Stochastic Logic Programs , 1996 .
[32] 瀬々 潤,et al. Traversing Itemset Lattices with Statistical Metric Pruning (小特集 「発見科学」及び一般演題) , 2000 .
[33] Taisuke Sato,et al. A Statistical Learning Method for Logic Programs with Distribution Semantics , 1995, ICLP.
[34] R. Mooney,et al. Explanation-Based Learning: An Alternative View , 1986, Machine Learning.
[35] Tom M. Mitchell,et al. Explanation-Based Generalization: A Unifying View , 1986, Machine Learning.
[36] Luc De Raedt,et al. Parameter estimation in ProbLog from annotated queries , 2010 .
[37] Michael I. Jordan,et al. Mean Field Theory for Sigmoid Belief Networks , 1996, J. Artif. Intell. Res..
[38] Hannu Toivonen,et al. Finding Frequent Substructures in Chemical Compounds , 1998, KDD.
[39] Antonis C. Kakas,et al. Special issue: abductive logic programming , 2000, The Journal of Logic Programming.
[40] S. Wrobel. First Order Theory Reenement , 1996 .
[41] Luc De Raedt,et al. Basic Principles of Learning Bayesian Logic Programs , 2008, Probabilistic Inductive Logic Programming.
[42] David Poole,et al. Logic programming, abduction and probability , 1993, New Generation Computing.
[43] Dan Suciu,et al. Efficient query evaluation on probabilistic databases , 2004, The VLDB Journal.
[44] Kate Revoredo,et al. Probabilistic First-Order Theory Revision from Examples , 2005, ILP.
[45] Nicola Fanizzi,et al. Ideal Theory Refinement under Object Identity , 2000, ICML.
[46] Evgeny Dantsin,et al. Probabilistic Logic Programs and their Semantics , 1990, RCLP.
[47] Luc De Raedt,et al. Probabilistic local pattern mining , 2008 .
[48] Luc De Raedt,et al. Condensed Representations for Inductive Logic Programming , 2004, KR.
[49] P. Bork,et al. Association of genes to genetically inherited diseases using data mining , 2002, Nature Genetics.
[50] Shinichi Morishita,et al. Transversing itemset lattices with statistical metric pruning , 2000, PODS '00.
[51] De Raedt,et al. Advances in Inductive Logic Programming , 1996 .
[52] J. Lloyd. Foundations of Logic Programming , 1984, Symbolic Computation.
[53] David Poole,et al. Abducing through negation as failure: stable models within the independent choice logic , 2000, J. Log. Program..
[54] Luc De Raedt,et al. Towards digesting the alphabet-soup of statistical relational learning , 2008 .
[55] Heikki Mannila,et al. Levelwise Search and Borders of Theories in Knowledge Discovery , 1997, Data Mining and Knowledge Discovery.
[56] Chris Clifton,et al. Query flocks: a generalization of association-rule mining , 1998, SIGMOD '98.
[57] Alan Bundy,et al. Explanation-Based Generalisation = Partial Evaluation , 1988, Artif. Intell..
[58] Pedro M. Domingos,et al. Markov Logic: An Interface Layer for Artificial Intelligence , 2009, Markov Logic: An Interface Layer for Artificial Intelligence.
[59] A. Antunes. Democracia e Cidadania na Escola: Do Discurso à Prática , 2008 .
[60] I. V. Ramakrishnan,et al. Efficient Access Mechanisms for Tabled Logic Programs , 1999, J. Log. Program..
[61] Raymond J. Mooney,et al. Inducing Deterministic Prolog Parsers from Treebanks: A Machine Learning Approach , 1994, AAAI.
[62] Krzysztof R. Apt,et al. Logic Programming , 1990, Handbook of Theoretical Computer Science, Volume B: Formal Models and Sematics.
[63] David Poole,et al. Probabilistic Horn Abduction and Bayesian Networks , 1993, Artif. Intell..
[64] Luc De Raedt,et al. Probabilistic Explanation Based Learning , 2007, ECML.
[65] Luc De Raedt,et al. Compressing probabilistic Prolog programs , 2007, Machine Learning.
[66] R. Costa,et al. YAP user''s manual , 1989 .
[67] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.