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| Transfer Learning mit LDA-Themenmodell× | LDA-Themenmodell× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2003–2013 | 2003 |
| Urheber≠ | Chen, Z. et al. / Blei, D. M. et al. | Blei, D. M., Ng, A. Y., & Jordan, M. I. |
| Typ≠ | Transfer learning applied to probabilistic topic model | Probabilistic generative topic model |
| Wegweisende Quelle≠ | Chen, Z., Mukherjee, A., Liu, B., Hsu, M., Malas, M., & Wang, S. (2013). Leveraging multi-domain prior knowledge in topic models. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI-13), pp. 2071–2077. link ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| Aliasnamen | LDA transfer learning, domain-adaptive LDA, knowledge transfer LDA, cross-domain LDA | LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | Transfer Learning with LDA Topic Model applies knowledge from a well-studied source domain to guide Latent Dirichlet Allocation inference on a data-scarce target domain. By injecting source-derived topic priors into the Dirichlet hyperparameters, the method produces coherent, domain-relevant topics even when target-domain text is limited, reducing the volume of labelled or unlabelled data required for meaningful results. | 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. |
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