Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Selected papers of international conference on Fifth generation computer systems 92
Learning probabilistic datalog rules for information classification and transformation
Proceedings of the tenth international conference on Information and knowledge management
Machine Learning
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
ACM SIGKDD Explorations Newsletter
Creating probabilistic databases from information extraction models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Compressing probabilistic Prolog programs
Machine Learning
Probabilistic Explanation Based Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
Towards learning stochastic logic programs from proof-banks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Mean field theory for sigmoid belief networks
Journal of Artificial Intelligence Research
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning probabilistic logic models from probabilistic examples
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Basic principles of learning Bayesian logic programs
Probabilistic inductive logic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Link discovery in graphs derived from biological databases
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
On the Efficient Execution of ProbLog Programs
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
ProbLog Technology for Inference in a Probabilistic First Order Logic
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
On the implementation of the probabilistic logic programming language problog
Theory and Practice of Logic Programming
Learning the parameters of probabilistic logic programs from interpretations
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Probabilistic rule learning in nonmonotonic domains
CLIMA'11 Proceedings of the 12th international conference on Computational logic in multi-agent systems
Learning the structure of probabilistic logic programs
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Link and node prediction in metabolic networks with probabilistic logic
Bisociative Knowledge Discovery
MCINTYRE: A Monte Carlo System for Probabilistic Logic Programming
Fundamenta Informaticae - Special Issue on the Italian Conference on Computational Logic: CILC 2011
Expectation maximization over binary decision diagrams for probabilistic logic programs
Intelligent Data Analysis
Hi-index | 0.00 |
We introduce the problem of learning the parameters of the probabilistic database ProbLog. Given the observed success probabilities of a set of queries, we compute the probabilities attached to facts that have a low approximation error on the training examples as well as on unseen examples. Assuming Gaussian error terms on the observed success probabilities, this naturally leads to a least squares optimization problem. Our approach, called LeProbLog, is able to learn both from queries and from proofs and even from both simultaneously. This makes it flexible and allows faster training in domains where the proofs are available. Experiments on real world data show the usefulness and effectiveness of this least squares calibration of probabilistic databases.