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Latent Dirichlet distribution topics model LDA
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LDA is a document theme generation model, also known as a three-layer Bayesian probability model for three-layer structure, themes, and document that contains the word. Document to the topic subject to Dirichlet distributions, subject to terms to be polynomial distributed.
LDA is an unsupervised machine learning techniques can be used to identify large scale documents (document collection) or the corpus (corpus) lurking in the theme information. It uses the word bag (bag of words) method, this method treats each document as a vector of frequency, which converts text information for easy modeling of digital information. But Word bags is not between the words and the order into account, this simplifies the complexity of the issues and has provided an opportunity for the improvement of the model of. Each document represents a number of topics consisting of a probability distribution, and each topic representing a lot of words that form a
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