In this paper, we propose a novel, efficient similarity search technique for general GMMs without independence assumption for the attributes, named SUDN, which ...
A two dimensional non-axis parallel Gaussian Mixture Model object containing m = 4 Gaussian distributions (G1, ..., G4). Each Gaussian consists of a weight ...
Haegler K., Fiedler F., Böhm C.: Searching Uncertain Data Represented by Non-Axis Parallel Gaussian Mixture Models, In Proc. ... Parallel Similarity Search in ...
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Jan 31, 2024 · Katrin Haegler, Frank Fiedler, Christian Böhm: Searching Uncertain Data Represented by Non-axis Parallel Gaussian Mixture Models.
Jan 31, 2018 · Böhm, “Searching uncertain data represented by non- axis parallel Gaussian Mixture Models,” in ICDE, 2012, pp. 246–257. [65] C. Böhm, P ...
May 12, 2024 · An appealing feature of Gaussian mixture models is their tractability, that is, they can be learned efficiently and exactly from data, and also ...
Missing: Uncertain | Show results with:Uncertain
A limitation to this approach is that there is no uncertainty measure or probability that tells us how much a data point is associated with a specific cluster.
Missing: axis Parallel
Oct 31, 2023 · Gaussian mixture models (GMMs) are widely used for modelling stochastic problems. Indeed, a wide diversity of packages have been developed in R.
Abstract. How can we train a statistical mixture model on a massive data set? In this work we show how to construct coresets for mixtures of Gaussians.
Metadata as JSON. Searching Uncertain Data Represented by Non-axis Parallel Gaussian Mixture Models. PROCEEDINGS ARTICLE published April 2012 in 2012 IEEE ...