Maximum Entropy Modeling with Clausal Constraints
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
Constructing informative priors using transfer learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Bottom-up learning of Markov logic network structure
Proceedings of the 24th international conference on Machine learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Mapping and revising Markov logic networks for transfer learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Relational macros for transfer in reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Building re-usable dictionary repositories for real-world text mining
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
ACM Transactions on Information Systems (TOIS)
Double-bootstrapping source data selection for instance-based transfer learning
Pattern Recognition Letters
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A central goal of transfer learning is to enable learning when training data from the domain of interest is limited. Yet, work on transfer across relational domains has so far focused on the case where there is a significant amount of target data. This paper bridges this gap by studying transfer when the amount of target data is minimal and consists of information about just a handful of entities. In the extreme case, only a single entity is known. We present the SR2LR algorithm that finds an effective mapping of predicates from a source model to the target domain in this setting and thus renders pre-existing knowledge useful to the target task. We demonstrate SR2LR's effectiveness in three benchmark relational domains on social interactions and study its behavior as information about an increasing number of entities becomes available.