Name
Title | ||
|---|---|---|
Transfer learning using task-level features with application to information retrieval |
Abstract | ||
|---|---|---|
We propose a probabilistic transfer learning model that uses task-level features to control the task mixture selection in a hierarchical Bayesian model. These task-level features, although rarely used in existing approaches, can provide additional information to model complex task distributions and allow effective transfer to new tasks especially when only limited number of data are available. To estimate the model parameters, we develop an empirical Bayes method based on variational approximation techniques. Our experiments on information retrieval show that the proposed model achieves significantly better performance compared with other transfer learning methods. |
Year | Venue | Keywords |
|---|---|---|
2009 | IJCAI | information retrieval show,hierarchical bayesian model,probabilistic transfer,new task,additional information,model parameter,complex task distribution,task-level feature,effective transfer,information retrieval,transfer learning |
Field | DocType | Citations |
Divergence-from-randomness model,Data mining,Bayesian inference,Multi-task learning,Information retrieval,Computer science,Transfer of learning,Artificial intelligence,Probabilistic logic,Empirical Bayes method,Machine learning | Conference | 3 |
PageRank | References | Authors |
0.43 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rong Yan | 1 | 2019 | 104.99 |
| Jian Zhang | 2 | 222 | 12.92 |