The structure-mapping engine: algorithm and examples
Artificial Intelligence
Interactive Concept-Learning and Constructive Induction by Analogy
Machine Learning
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relational learning with statistical predicate invention: better models for hypertext
Machine Learning - Special issue on inducive logic programming
Relational Data Mining
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Statistical predicate invention
Proceedings of the 24th international conference on Machine learning
Mapping and revising Markov logic networks for transfer learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
An integrated approach to learning bayesian networks of rules
ECML'05 Proceedings of the 16th European conference on Machine Learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Multi-view transfer learning with a large margin approach
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Sentiment detection with auxiliary data
Information Retrieval
Location-based reasoning about complex multi-agent behavior
Journal of Artificial Intelligence Research
ACM SIGKDD Explorations Newsletter
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Standard inductive learning requires that training and test instances come from the same distribution. Transfer learning seeks to remove this restriction. In shallow transfer, test instances are from the same domain, but have a different distribution. In deep transfer, test instances are from a different domain entirely (i.e., described by different predicates). Humans routinely perform deep transfer, but few learning systems, if any, are capable of it. In this paper we propose an approach based on a form of second-order Markov logic. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. Using this approach, we have successfully transferred learned knowledge among molecular biology, social network and Web domains. The discovered patterns include broadly useful properties of predicates, like symmetry and transitivity, and relations among predicates, such as various forms of homophily.