Machine learningDeep learning / NLP / CV

Modeliranje tema

Modeliranje tema (Topic Modeling) je obitelj nadziranih probabilističkih tehnika za otkrivanje latentne tematske strukture u velikim tekstualnim zbirkama. Učeći koje se riječi imaju tendenciju pojavljivati zajedno, modeli poput Latent Dirichlet Allocation (LDA) automatski otkrivaju koherentne teme — svaka predstavljena kao distribucija nad vokabularom — 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/hr/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 s https://scholargate.app/hr/deep-learning/topic-modeling · Skup podataka: https://doi.org/10.5281/zenodo.20539026