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?????????? - ???????. 2018 ? 10 ? 12 ?????. 1. ????. 2. ??????????. 3. ????. 4. ???????. 5. ???. ?. ?????. ?? - ????????????????????. ???????????????????????. ???????????????????????. ????????????????????? ... ? ?? ???? ? - ????3??????????????????R????1+1?????????????????? F??G??2022?10???????????F????????G? ... EDITOR'S - ?????????... ??????????????????????. ?????????????????????????????????????????????????????. ?? ... ????????? - ???????????????????????????????? R???. G???. 3????. ????????. ????????. The Predictive Power of Social Media Data - CBS Research PortalNaive Bayes classifier is a linear classifier based on the popular Bayes' probability theorem, and it is known for creating simple yet well performing models, ... Machine Learning Techniques for Social Media Analysis - POLITesiWe propose a new model, the latent position cluster model, under which the probability of a tie between two actors depends on the distance. Model-based clustering for social networksIt combines three different approaches, including, content-based approach, Bayesian-classifier approach, and the rule-based approach. Probabilistic topic models - Gregory EadyTopic modeling algorithms can be adapted to many kinds of data. Among other applications, they have been used to find patterns in genetic data, images, and ... Scalable and Efficient Probabilistic Topic Model Inference for ...The computational revolution and the increased accessibility of textual data give rise to new possibilities for these scientific fields. Many new corpora, or ... N-gram Language Models - Stanford UniversityAs we see above, the more information the n-gram gives us about the word sequence, the higher the probability the n-gram will assign to the string. A trigram. Introduction to Probabilistic Topic ModelsProbabilistic topic models are a suite of algorithms whose aim is to discover the hidden thematic structure in large archives of documents.
