en:tools:gibbstopicmodels
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| - | =====Topic Models（Gibbs Sampling）===== | ||
| - | ====Introduction==== | ||
| - | Topic model is family of generative probabilistic models for discovering the main themes from a collection of documents. For more elaborate and detailed surveys, we refer the readers to [1]. Examples of topic models include Latent Dirichlet Allocation (LDA) [2][3][4], Author-Topic (AT) model [5][6][7], and co-Author-Topic (coAT) model [8], and many others. | ||
| - | The inference for topic models usually cannot be done exactly. A variety of approximate inference algorithms have appeared in recent years, such as stochastic variational inference, mean-field variational methods, expectation propagation, and Monte Carlo Markov chain sampling (MCMC). In this toolbox, Gibbs sampling, a special case of MCMC, is utilized, since it provides a simple method for obtaining parameter estimates under Dirichlet priors and allows combination of estimates from several local maxima of the posterior distribution. | ||
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| - | ====Programming Language==== | ||
| - | JAVA | ||
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| - | ====Source Codes==== | ||
| - | https://github.com/pzczxs/GibbsTopicModels | ||
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| - | ====Additional Information==== | ||
| - | If you find this toolbox useful, please cite GibbsTopicModels as follows: | ||
| - | *Xin An, Shuo Xu, Yali Wen, and Mingxing Hu, 2014. [[http://dx.doi.org/10.1155/2014/820715|A Shared Interest Discovery Model for Co-author Relationship in SNS]]. //International Journal of Distributed Sensor Networks//, Vol. 2014, pp. 1-9. ''{{xushuo:papers:axwh14.pdf|PDF}}'' | ||
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| - | ====References==== | ||
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| - | ~~DISCUSSION~~ | ||
en/tools/gibbstopicmodels.1492667055.txt.gz · 最后更改: 2017/04/20 13:44 由 pzczxs