Results 21 to 30 of about 88,242 (260)
Gaussian mixture model of heart rate variability. [PDF]
PLoS ONE, 2012 Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition.
Tommaso Costa +2 more
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On the Properties of Gaussian Copula Mixture Models
Advances in Artificial Intelligence and Machine Learning, 2023 11 pages paper for theoretical properties and new algorithms for ...
Ke Wan 0001, Alain L. Kornhauser
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Model Selection for Gaussian Mixture Models [PDF]
Statistica Sinica, 2017 This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for model selection of finite multivariate Gaussian mixture models. The proposed method is shown to be statistically consistent in determining of the number of components. A modified
Peng, H., Huang, T., Zhang, K.
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Quantum-like Gaussian mixture model [PDF]
Soft Computing, 2021 Abstract A new concept of a quantum-like mixture model is introduced. It describes the mixture distribution with the assumption that a point is generated by each Gaussian at the same time. The decision boundary of a quantum-like mixture Gaussian corresponds as well to the separation of probabilities for the switching Kalman filter. The quantum-
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Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
Algorithms, 2023 Gaussian mixture modeling is a generative probabilistic model that assumes that the observed data are generated from a mixture of multiple Gaussian distributions. This mixture model provides a flexible approach to model complex distributions that may not
Luca Scrucca
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Optimal Transport for Gaussian Mixture Models [PDF]
IEEE Access, 2019 We present an optimal mass transport framework on the space of Gaussian mixture models, which are widely used in statistical inference. Our method leads to a natural way to compare, interpolate and average Gaussian mixture models. Basically, we study such models on a certain submanifold of probability densities with certain structure. Different aspects
Yongxin Chen 0002 +2 more
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Reinforcement Learning with a Gaussian mixture model [PDF]
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010 Recent approaches to Reinforcement Learning (RL) with function approximation include Neural Fitted Q Iteration and the use of Gaussian Processes. They belong to the class of fitted value iteration algorithms, which use a set of support points to fit the value-function in a batch iterative process.
Agostini, Alejandro Gabriel +1 more
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Continuous Gaussian mixture modeling [PDF]
, 1997 When the projection of a collection of samples onto a subset of basis feature vectors has a Gaussian distribution, those samples have a generalized projective Gaussian distribution (GPGD). GPGDs arise in a variety of medical images as well as some speech recognition problems.
Stephen R. Aylward, Stephen M. Pizer
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Supervoxel Segmentation with Voxel-Related Gaussian Mixture Model
Sensors, 2018 Extended from superpixel segmentation by adding an additional constraint on temporal consistency, supervoxel segmentation is to partition video frames into atomic segments.
Zhihua Ban, Zhong Chen, Jianguo Liu
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Regression with Gaussian Mixture ModelsApplied to Track Fitting
Instruments, 2020 This note describes the application of Gaussian mixture regression to track fitting with a Gaussian mixture model of the position errors. The mixture model is assumed to have two components with identical component means.
Rudolf Frühwirth
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