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- W3204672119 abstract "The LDA model is a total probability generation model for analyzing a large number of documents. It extends PLSA, another text analysis model. In this model, each document is treated as a topic hybrid model, and the topic's proportional prior distribution is a Dirichlet distribution. The LDA model does not reflect complex dependencies between underlying topics. Based on LDA, this paper introduces a new topic generation model, DMA (Dirichlet Mixture Allocation), which models document collections more accurately than LDA when documents are obtained in multiple classes. In this paper, we build a probabilistic topic model of DMA, use the method of variational inference to approximate each parameter of the model, study the model, and finally solve the estimation of each parameter." @default.
- W3204672119 created "2021-10-11" @default.
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- W3204672119 date "2021-09-01" @default.
- W3204672119 modified "2023-10-16" @default.
- W3204672119 title "Construction of Dirichlet Mixture Allocation total probability model based on multiple class text analysis" @default.
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- W3204672119 doi "https://doi.org/10.1088/1742-6596/2031/1/012055" @default.
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