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| 토픽 모델링을 이용한 전이 학습× | LDA 토픽 모델× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2010s | 2003 |
| 창시자≠ | Pan, S. J. & Yang, Q. (transfer learning survey); combined with Blei et al. (LDA, 2003) | Blei, D. M., Ng, A. Y., & Jordan, M. I. |
| 유형≠ | Cross-domain adaptation of topic models | Probabilistic generative topic model |
| 원전≠ | Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| 별칭 | domain-transfer topic modeling, pretrained topic transfer, cross-domain topic adaptation, TL-LDA | LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model |
| 관련 | 5 | 5 |
| 요약≠ | 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. | Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words. |
| ScholarGate데이터셋 ↗ |
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