Results 21 to 30 of about 1,959,808 (288)
Statistical Compressed Sensing of Gaussian Mixture Models [PDF]
, 2011 A novel framework of compressed sensing, namely statistical compressed sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribution, and achieving accurate reconstruction on average, is introduced.
Sapiro, Guillermo, Yu, Guoshen
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Incremental Learning of Nonparametric Bayesian Mixture Models [PDF]
, 2008 Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group before learning can begin.
Gomes, Ryan +2 more
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Machine Learning based on Probabilistic Models Applied to Medical Data: The Case of Prostate Cancer
Journal of Innovation Information Technology and Application, 2023 The growth in the amount of data in companies puts analysts in difficulties when extracting hidden knowledge from data. Several models have emerged that focus on the notion of distances while ignoring the notion of conditional probability density.
Anaclet Tshikutu Bikengela +4 more
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CoRR, 2016 Casting neural networks in generative frameworks is a highly sought-after endeavor these days. Contemporary methods, such as Generative Adversarial Networks, capture some of the generative capabilities, but not all. In particular, they lack the ability of tractable marginalization, and thus are not suitable for many tasks.
Or Sharir +3 more
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, 2019 Mixtures of experts models provide a framework in which covariates may be included in mixture models. This is achieved by modelling the parameters of the mixture model as functions of the concomitant covariates. Given their mixture model foundation, mixtures of experts models possess a diverse range of analytic uses, from clustering observations to ...
Gormley, Isobel Claire +1 more
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On a Mixture Autoregressive Model
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2000 Summary We generalize the Gaussian mixture transition distribution (GMTD) model introduced by Le and co-workers to the mixture autoregressive (MAR) model for the modelling of non-linear time series. The models consist of a mixture of K stationary or non-stationary AR components. The advantages of the MAR model over the GMTD model include
Wong, CS, Li, WK
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Lessons Learned From the Training of GANs on Artificial Datasets
IEEE Access, 2020 Generative Adversarial Networks (GANs) have made great progress in synthesizing realistic images in recent years. However, they are often trained on image datasets with either too few samples or too many classes belonging to different data distributions.
Shichang Tang
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Adaptive Seeding for Gaussian Mixture Models
, 2017 We present new initialization methods for the expectation-maximization algorithm for multivariate Gaussian mixture models. Our methods are adaptions of the well-known $K$-means++ initialization and the Gonzalez algorithm.
AP Dempster +13 more
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Incrementally Learned Mixture Models for GNSS Localization
, 2020 GNSS localization is an important part of today's autonomous systems, although it suffers from non-Gaussian errors caused by non-line-of-sight effects. Recent methods are able to mitigate these effects by including the corresponding distributions in the ...
Pfeifer, Tim, Protzel, Peter
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Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2012 The aim of this paper is to present and discuss the power of the exact likelihood ratio homogeneity testing procedure of the number of components k in the exponential mixture.
Luboš Střelec, Milan Stehlík
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