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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); | + | |
- | - | + | |
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: | - 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: | ||
- | - T. Denoeux, Maximum likelihood estimation from Uncertain Data in the Belief Function Framework, IEEE Transactions on Knowledge and Data Engineering | + | - T. Denoeux. Maximum likelihood estimation from Uncertain Data in the Belief Function Framework. IEEE Transactions on Knowledge and Data Engineering, |
{{en: | {{en: | ||