Machine learningDeep learning / NLP / CV

Modeliranje tema

Modeliranje tema je porodica nadziranih probabilističkih tehnika za otkrivanje latentne tematske strukture u velikim tekstualnim kolekcijama. Učeći koje se reči sklonju da se javljaju zajedno, modeli kao što je Latent Dirichlet Allocation (LDA) automatski izdvajaju koherentne teme — svaka predstavljena kao distribucija nad rečnikom — bez potrebe za označenim podacima.

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Izvori

  1. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link
  2. Hofmann, T. (1999). Probabilistic Latent Semantic Analysis. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI), 289–296. link

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Topic Modeling (Probabilistic Latent Semantic Analysis and Latent Dirichlet Allocation). ScholarGate. https://scholargate.app/sr/deep-learning/topic-modeling

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Citirana u

ScholarGateTopic Modeling (Topic Modeling (Probabilistic Latent Semantic Analysis and Latent Dirichlet Allocation)). Preuzeto 2026-06-15 sa https://scholargate.app/sr/deep-learning/topic-modeling · Skup podataka: https://doi.org/10.5281/zenodo.20539026