差别

这里会显示出您选择的修订版和当前版本之间的差别。

--- zh:tools:gibbstopicmodels [2017/04/20 13:38]
pzczxs 创建
+++ zh:tools:gibbstopicmodels [2022/06/30 11:30] (当前版本)
pzczxs 讨论状态变化了
@@ 行 1: / 行 1: @@
-=====主题模型（Gibbs采样）=====
+=====Topic Models（Gibbs Sampling）=====
-目前实现的模型包括：LDA、AT、coAT等。
+====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.
-编程语言：JAVA
+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.
-源代码：https://github.com/pzczxs/GibbsTopicModels
+====Programming Language====
+JAVA
-相关文档：
+====Source Codes====
-  *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}}''
+https://github.com/pzczxs/GibbsTopicModels
+====Citation 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 Coauthor Relationship in SNS]]. //International Journal of Distributed Sensor Networks//, Vol. 2014, No. 820715, pp. 1-9. ''{{xushuo:papers:axwh14.pdf|PDF}}''
+====References====
+  -David M. Blei, 2012. [[http://dx.doi.org/10.1145/2133806.2133826|Introduction to Probabilistic Topic Models]]. //Communications of the ACM//, Vol. 55, No. 4, pp. 77-84.
+  -David M. Blei, Andrew Y. Ng, and Michael I. Jordan, 2003. [[http://jmlr.csail.mit.edu/papers/v3/blei03a.html|Latent Dirichlet Allocation]]. //Journal of Machine Learning Research//, Vol. 3, No. Jan, pp. 993-1022.
+  -Thomas L. Griffiths and Mark Steyvers, 2004. [[http://www.pnas.org/content/101/suppl_1/5228.abstract|Finding Scientific Topics]]. //Proceedings of the National Academy of Sciences of the United States of America//, Vol. 101, No. Suppl, pp. 5228-5235.
+  -Gregor Heinrich, 2009. [[http://www.arbylon.net/publications/text-est2.pdf|Parameter Estimation for Text Analysis]]. //Technical Report Version 2.9//. vsonix GmbH and University of Leipzig.
+  -Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, and Padhraic Smyth, 2004. [[https://mimno.infosci.cornell.edu/info6150/readings/398.pdf|The Author-Topic Model for Authors and Documents]]. //Proceedings of the 20th International Conference on Uncertainty in Artificial Intelligence//, pp. 487-494.
+  -Mark Steyvers, Padhraic Smyth, and Thomas Griffiths, 2004. [[http://psiexp.ss.uci.edu/research/papers/author_topics_kdd.pdf|Probabilistic Author-Topic Models for Information Discovery]]. //Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining//, pp. 306-315.
+  -Michal Rosen-Zvi, Chaitanya Chemudugunta, Thomas Griffiths, and Padhraic Smyth, and Mark Steyvers, 2010. [[https://cocosci.berkeley.edu/tom/papers/AT_tois.pdf|Learning Author-Topic Models from Text Corpora]]. //ACM Transactions on Information Systems//, Vol. 28, No. 1, pp. 1-38.
+  -Xin An, Shuo Xu, Yali Wen, and Mingxing Hu, 2014. [[http://dx.doi.org/10.1155/2014/820715|A Shared Interest Discovery Model for Coauthor Relationship in SNS]]. //International Journal of Distributed Sensor Networks//, Vol. 2014, No. 820715, pp. 1-9.
+~~DISCUSSION:closed~~

硕风徐徐

用户工具

站点工具

差别

页面工具