Towards Multistrategic Statistical Relational Learning
暂无分享,去创建一个
[1] David Poole,et al. Probabilistic Horn Abduction and Bayesian Networks , 1993, Artif. Intell..
[2] Nils J. Nilsson,et al. Probabilistic Logic * , 2022 .
[3] Andrew McCallum,et al. Efficiently Inducing Features of Conditional Random Fields , 2002, UAI.
[4] John D. Lafferty,et al. Inducing Features of Random Fields , 1995, IEEE Trans. Pattern Anal. Mach. Intell..
[5] James Cussens,et al. Parameter Estimation in Stochastic Logic Programs , 2001, Machine Learning.
[6] Nir Friedman,et al. Learning Belief Networks in the Presence of Missing Values and Hidden Variables , 1997, ICML.
[7] Alon Y. Halevy,et al. P-CLASSIC: A Tractable Probablistic Description Logic , 1997, AAAI/IAAI.
[8] Keith L. Clark,et al. Negation as Failure , 1987, Logic and Data Bases.
[9] Gordon Plotkin,et al. A Note on Inductive Generalization , 2008 .
[10] Pedro M. Domingos,et al. Efficient Weight Learning for Markov Logic Networks , 2007, PKDD.
[11] Robert A. Kowalski,et al. Abduction Compared with Negation by Failure , 1989, ICLP.
[12] Matthew Richardson,et al. Markov logic networks , 2006, Machine Learning.
[13] Pedro M. Domingos,et al. A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC , 2008, AAAI.
[14] Fernando Pereira,et al. Shallow Parsing with Conditional Random Fields , 2003, NAACL.
[15] Stephen Muggleton,et al. Inverse entailment and progol , 1995, New Generation Computing.
[16] Evelina Lamma,et al. Cooperation of Abduction and Induction in Logic Programming , 2000 .
[17] F. Glover,et al. Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.
[18] David Poole,et al. A Logical Framework for Default Reasoning , 1988, Artif. Intell..
[20] Raymond J. Mooney,et al. Bottom-up learning of Markov logic network structure , 2007, ICML '07.
[21] Joost N. Kok,et al. Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings , 2007, PKDD.
[22] Stefano Ferilli,et al. Discriminative Structure Learning of Markov Logic Networks , 2008, ILP.
[23] Stefano Ferilli,et al. Structure Learning of Markov Logic Networks through Iterated Local Search , 2008, ECAI.
[24] P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .
[25] Ryszard S. Michalski,et al. Inferential Theory of Learning: Developing Foundations for Multistrategy Learning , 1992 .
[26] De Raedt,et al. Advances in Inductive Logic Programming , 1996 .
[27] Jorge Nocedal,et al. On the limited memory BFGS method for large scale optimization , 1989, Math. Program..
[28] Chad Cumby Dan Roth,et al. Feature Extraction Languages for Propositionalized Relational Learning , 2003 .
[29] C. Geyer,et al. Constrained Monte Carlo Maximum Likelihood for Dependent Data , 1992 .
[30] S. Muggleton. Stochastic Logic Programs , 1996 .
[31] Peter Haddawy,et al. Answering Queries from Context-Sensitive Probabilistic Knowledge Bases , 1997, Theor. Comput. Sci..
[32] Saso Dzeroski,et al. Inductive Logic Programming: Techniques and Applications , 1993 .
[33] Stuart J. Russell,et al. Approximate inference for first-order probabilistic languages , 2001, IJCAI.
[34] P. Schönemann. On artificial intelligence , 1985, Behavioral and Brain Sciences.
[35] Raymond Reiter,et al. A Logic for Default Reasoning , 1987, Artif. Intell..
[36] Michael R. Genesereth,et al. Logical foundations of artificial intelligence , 1987 .
[37] JOHANNES FÜRNKRANZ,et al. Separate-and-Conquer Rule Learning , 1999, Artificial Intelligence Review.
[38] Taisuke Sato,et al. A Viterbi-like algorithm and EM learning for statistical abduction , 2000 .
[39] Ben Taskar,et al. Introduction to statistical relational learning , 2007 .
[40] Pedro M. Domingos,et al. Markov Logic in Infinite Domains , 2007, UAI.
[41] Ben Taskar,et al. Discriminative Probabilistic Models for Relational Data , 2002, UAI.
[42] James Cussens,et al. CLP(BN): Constraint Logic Programming for Probabilistic Knowledge , 2002, Probabilistic Inductive Logic Programming.
[43] Pedro M. Domingos,et al. Sound and Efficient Inference with Probabilistic and Deterministic Dependencies , 2006, AAAI.
[44] Ehud Shapiro,et al. Algorithmic Program Debugging , 1983 .
[45] Pedro M. Domingos,et al. Discriminative Training of Markov Logic Networks , 2005, AAAI.
[46] Michael Wooldridge,et al. Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, IJCAI 97, Nagoya, Japan, August 23-29, 1997, 2 Volumes , 1997, IJCAI.
[47] Shan-Hwei Nienhuys-Cheng,et al. Foundations of Inductive Logic Programming , 1997, Lecture Notes in Computer Science.
[48] K. J. Evans. Representing and Reasoning with Probabilistic Knowledge , 1993 .
[49] Robert P. Goldman,et al. From knowledge bases to decision models , 1992, The Knowledge Engineering Review.
[50] A. Aliseda. Abductive and Inductive Reasoning: Essays on their Relation and Integration , 2000 .
[51] Luc De Raedt,et al. Probabilistic Inductive Logic Programming - Theory and Applications , 2008, Probabilistic Inductive Logic Programming.
[52] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[53] Paolo Mancarella,et al. Abductive Logic Programming , 1992, LPNMR.
[54] J. R. Quinlan. Learning Logical Definitions from Relations , 1990 .
[55] Andreas Arvanitis,et al. Abduction with Stochastic Logic Programs based on a Possible Worlds Semantics , 2006 .
[56] Luc De Raedt,et al. Logical Settings for Concept-Learning , 1997, Artif. Intell..
[57] Johann Eder,et al. Logic and Databases , 1992, Advanced Topics in Artificial Intelligence.
[58] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[59] Luc De Raedt,et al. Clausal Discovery , 1997, Machine Learning.
[60] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[61] Pedro M. Domingos,et al. Learning the structure of Markov logic networks , 2005, ICML.
[62] J. Besag. Statistical Analysis of Non-Lattice Data , 1975 .
[63] Luc De Raedt,et al. Towards Combining Inductive Logic Programming with Bayesian Networks , 2001, ILP.
[64] Helena Ramalhinho Dias Lourenço,et al. Iterated Local Search , 2001, Handbook of Metaheuristics.
[65] Luc De Raedt,et al. Integrating Naïve Bayes and FOIL , 2007, J. Mach. Learn. Res..
[66] Fabrizio Riguzzi,et al. Learning with Abduction , 1997, ILP.
[67] Raymond J. Mooney,et al. Discriminative structure and parameter learning for Markov logic networks , 2008, ICML '08.
[68] Lise Getoor,et al. Learning Probabilistic Relational Models , 1999, IJCAI.
[69] Nicola Fanizzi,et al. Multistrategy Theory Revision: Induction and Abduction in INTHELEX , 2004, Machine Learning.
[70] Joseph Y. Halpern. An Analysis of First-Order Logics of Probability , 1989, IJCAI.
[71] Lyle H. Ungar,et al. Structural Logistic Regression for Link Analysis , 2003 .
[72] Stephen Muggleton,et al. Abductive Stochastic Logic Programs for Metabolic Network Inhibition Learning , 2007, MLG.
[73] Fabrizio Riguzzi,et al. Abductive concept learning , 2000, New Generation Computing.
[74] Thomas Stützle,et al. Stochastic Local Search: Foundations & Applications , 2004 .
[75] Taisuke Sato,et al. PRISM: A Language for Symbolic-Statistical Modeling , 1997, IJCAI.