Name
Abstract | ||
|---|---|---|
We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLR are first-order formulae with associated weight vectors instead of scalar weights. We turn the problem of learning RLR to learning these vector-weighted formulae and develop a learning algorithm based on the recently successful functional-gradient boosting methods for probabilistic logic models. We derive the functional gradients and show how weights can be learned simultaneously in an efficient manner. Our empirical evaluation on standard data sets demonstrates the superiority of our approach over other methods for learning RLR. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s10618-021-00770-8 | DATA MINING AND KNOWLEDGE DISCOVERY |
Keywords | DocType | Volume |
Statistical relational learning, Boosting, Relational models, Functional Gradient Boosting, Probabilistic Machine Learning | Journal | 35 |
Issue | ISSN | Citations |
5 | 1384-5810 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nandini Ramanan | 1 | 2 | 2.16 |
| Kunapuli, Gautam | 2 | 136 | 12.32 |
| Tushar Khot | 3 | 168 | 9.84 |
| Bahareh Fatemi | 4 | 0 | 0.34 |
| Seyed Mehran Kazemi | 5 | 40 | 7.31 |
| David Poole | 6 | 2574 | 245.18 |
| Kristian Kersting | 7 | 1932 | 154.03 |
| Sriraam Natarajan | 8 | 482 | 49.32 |