Name
Abstract | ||
|---|---|---|
Transductive transfer learning is one special type of transfer learning problem, in which abundant labeled examples are available in the source domain and only unlabeled examples are available in the target domain. It easily finds applications in spam filtering, microblogging mining, and so on. In this paper, we propose a general framework to solve the problem by mapping the input features in both the source domain and the target domain into a shared latent space and simultaneously minimizing the feature reconstruction loss and prediction loss. We develop one specific example of the framework, namely latent large-margin transductive transfer learning algorithm, and analyze its theoretic bound of classification loss via Rademacher complexity. We also provide a unified view of several popular transfer learning algorithms under our framework. Experiment results on one synthetic dataset and three application datasets demonstrate the advantages of the proposed algorithm over the other state-of-the-art ones. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10115-013-0647-5 | Knowl. Inf. Syst. |
Keywords | Field | DocType |
Transductive transfer learning, Large-margin approach, Rademacher complexity, Stochastic gradient descent | Transduction (machine learning),Data mining,Stochastic gradient descent,Semi-supervised learning,Inductive transfer,Computer science,Rademacher complexity,Transfer of learning,Filter (signal processing),Artificial intelligence,Machine learning,Scalability | Journal |
Volume | Issue | ISSN |
38 | 1 | 0219-3116 |
Citations | PageRank | References |
4 | 0.69 | 34 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Taha Bahadori | 1 | 383 | 19.60 |
| Yan Liu | 2 | 2551 | 189.16 |
| Dan Zhang | 3 | 461 | 22.17 |