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| Трансферно обучение с тематично моделиране× | Тематичен модел с НМФ× | |
|---|---|---|
| Област | Дълбоко обучение | Дълбоко обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2010s | 1999 |
| Създател≠ | Pan, S. J. & Yang, Q. (transfer learning survey); combined with Blei et al. (LDA, 2003) | Lee, D. D. & Seung, H. S. |
| Тип≠ | Cross-domain adaptation of topic models | Matrix factorization / unsupervised topic model |
| Основополагащ източник≠ | Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. DOI ↗ |
| Други названия | domain-transfer topic modeling, pretrained topic transfer, cross-domain topic adaptation, TL-LDA | NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model |
| Свързани≠ | 5 | 4 |
| Резюме≠ | Transfer Learning with Topic Modeling adapts topic structures discovered on a large or well-labeled source corpus to a related but distinct target domain where labeled data or large corpora are scarce. By reusing source-domain topic priors or pretrained embeddings as initialization, the approach produces richer, more coherent topics in the target domain than training from scratch. | Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics. |
| ScholarGateНабор от данни ↗ |
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