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• W29281623 abstract "AbstractProbabilistic topic modeling of text collections is a powerful tool for statistical text analysis. In this tutorial we introduce a novel non-Bayesian approach, called Additive Regularization of Topic Models. ARTM is free of redundant probabilistic assumptions and provides a simple inference for many combined and multi-objective topic models.KeywordsProbabilistic topic modelingRegularization of ill-posed inverse problemsStochastic matrix factorizationProbabilistic latent sematic analysisLatent Dirichlet AllocationEM-algorithm" @default.
• W29281623 created "2016-06-24" @default.
• W29281623 creator A5030626083 @default.
• W29281623 creator A5050048876 @default.
• W29281623 date "2014-01-01" @default.
• W29281623 modified "2023-10-16" @default.
• W29281623 title "Tutorial on Probabilistic Topic Modeling: Additive Regularization for Stochastic Matrix Factorization" @default.
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• W29281623 doi "https://doi.org/10.1007/978-3-319-12580-0_3" @default.
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