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AN ACCELERATED ALGORITHM FOR DENSITY ESTIMATION IN LARGE DATABASES USING GAUSSIAN MIXTURES
2007
Cybernetics and systems
Today, with the advances of computer storage and technology, there are huge datasets available, offering an opportunity to extract valuable information. Probabilistic approaches are specially suited to learn from data by representing knowledge as density functions. In this paper, we choose Gaussian Mixture Models (GMMs) to represent densities, as they possess great flexibility to adequate to a wide class of problems. The classical estimation approach for GMMs corresponds to the iterative
doi:10.1080/01969720601138928
fatcat:yheisqjfdffu5ly4ggecusmr6m