Name
Abstract | ||
|---|---|---|
Transfer learning is typically performed between problem instances within the same domain. We consider the problem of transferring across domains. To this effect, we adopt a probabilistic logic approach. First, our approach automatically identifies predicates in the target domain that are similar in their relational structure to predicates in the source domain. Second, it transfers the logic rules and learns the parameters of the transferred rules using target data. Finally, it refines the rules as necessary using theory refinement. Our experimental evidence supports that this transfer method finds models as good or better than those found with state-of-the-art methods, with and without transfer, and in a fraction of the time. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICDM.2015.138 | IEEE International Conference on DataMining |
Field | DocType | ISSN |
Data mining,Relational calculus,Instance-based learning,Multi-task learning,Inductive transfer,Statistical relational learning,Computer science,Transfer of learning,Probabilistic CTL,Artificial intelligence,Probabilistic logic,Machine learning | Conference | 1550-4786 |
Citations | PageRank | References |
3 | 0.39 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raksha Kumaraswamy | 1 | 5 | 2.81 |
| Phillip Odom | 2 | 29 | 5.09 |
| Kristian Kersting | 3 | 1932 | 154.03 |
| David B. Leake | 4 | 1369 | 121.60 |
| Sriraam Natarajan | 5 | 482 | 49.32 |