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en:software:e2m [2011/08/11 16:07]
tdenoeux
en:software:e2m [2013/02/28 11:55]
tdenoeux
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 The toolbox provides functions for the following problems: The toolbox provides functions for the following problems:
-Probability estimation from uncertain data (Bernoulli model); +  * Probability estimation from uncertain data (Bernoulli model); 
-Clustering of uncertain categorical data using the latent class model (with the possibility to use uncertain information on class labels); +  Clustering of uncertain categorical data using the latent class model (with the possibility to use uncertain information on class labels); 
- Clustering of uncertain continuous data using the Gaussian mixture model (with the possibility to use uncertain information on class labels).+  Clustering of uncertain continuous data using the Gaussian mixture model (with the possibility to use uncertain information on class labels). 
  
 References: References:
  
- - T. Denoeux. Maximum likelihood from evidential data: an extension of the EM algorithm. In C. Borgelt et al. (Eds), Combining soft computing and statistical methods in data analysis (Proceedings of SMPS 2010, Oviedo, Spain, September 28 - October 1, 2010), Advances in Intelligent and Soft Computing, pages 181-188, Springer, 2010. {{en:publi:id178.pdf|pdf}}+  - T. Denoeux. Maximum likelihood from evidential data: an extension of the EM algorithm. In C. Borgelt et al. (Eds), Combining soft computing and statistical methods in data analysis (Proceedings of SMPS 2010, Oviedo, Spain, September 28 - October 1, 2010), Advances in Intelligent and Soft Computing, pages 181-188, Springer, 2010. {{en:publi:id178.pdf|pdf}} 
 +  - T. Denoeux. Maximum likelihood estimation from Uncertain Data in the Belief Function Framework. IEEE Transactions on Knowledge and Data Engineering, Vol. 25, Issue 1, pages 119-130, 2013. {{en:publi:tkde2011.pdf|pdf}} 
 + 
 +{{en:software:e2m.zip|Download}}
  

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