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- W2735927662 abstract "There are a variety of models and algorithms that solves classification problems. Among these models, Maximum Gaussian Mixture Model (MGMM) is a model we proposed earlier that describes data using the maximum value of Gaussians. Expectation Maximization (EM) algorithm can be used to solve this model. In this paper, we propose a multiEM approach to solve MGMM and to train MGMM based classifiers. This approach combines multiple MGMMs solved by EM into a classifier. The classifiers trained with this approach on both artificial and real life datasets were tested to have good performance with 10-fold cross validation." @default.
- W2735927662 created "2017-07-21" @default.
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- W2735927662 date "2016-12-01" @default.
- W2735927662 modified "2023-10-18" @default.
- W2735927662 title "Maximum Gaussian Mixture Model for Classification" @default.
- W2735927662 cites W1596717185 @default.
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- W2735927662 doi "https://doi.org/10.1109/itme.2016.0139" @default.
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