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      Interactive topic modeling

      , , ,
      Machine Learning
      Springer Nature

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          Advances in prospect theory: Cumulative representation of uncertainty

          Journal of Risk and Uncertainty, 5(4), 297-323
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            Finding scientific topics.

            A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.
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              Algorithm 457: finding all cliques of an undirected graph

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                Author and article information

                Journal
                Machine Learning
                Mach Learn
                Springer Nature
                0885-6125
                1573-0565
                June 2014
                October 19 2013
                : 95
                : 3
                : 423-469
                Article
                10.1007/s10994-013-5413-0
                716f13aa-9d52-44c8-a6e3-70f793ddc63d
                © 2013
                Product
                Self URI (article page): http://link.springer.com/10.1007/s10994-013-5413-0

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