Results 1 to 10 of about 102,242 (306)
A Fast Incremental Gaussian Mixture Model. [PDF]
PLoS ONE, 2015 This work builds upon previous efforts in online incremental learning, namely the Incremental Gaussian Mixture Network (IGMN). The IGMN is capable of learning from data streams in a single-pass by improving its model after analyzing each data point and ...
Rafael Coimbra Pinto +1 more
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Variational Autoencoder With Optimizing Gaussian Mixture Model Priors
IEEE Access, 2020 The latent variable prior of the variational autoencoder (VAE) often utilizes a standard Gaussian distribution because of the convenience in calculation, but has an underfitting problem.
Chunsheng Guo +5 more
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Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model [PDF]
Frontiers in Neuroscience, 2022 In this paper, a method for medical image registration based on the bounded generalized Gaussian mixture model is proposed. The bounded generalized Gaussian mixture model is used to approach the joint intensity of source medical images. The mixture model
Jingkun Wang +6 more
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A Novel Approach for Gaussian Mixture Model Clustering Based on Soft Computing Method
IEEE Access, 2021 Determining the number of clusters in a data set is a significant and difficult problem in cluster analysis. In this study, a new model-based clustering approach is proposed for the estimation of the number of clusters. In the proposed method, the number
Maruf Gogebakan
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Responsible Gaussian Model: Matrix-Based Approximation of Gaussian Mixture Model
IEEE AccessMechanisms of deep learning are often viewed as a unclear structure and are difficult to interpret or control precisely using mathematical or engineering principles.
Wataru Obayashi +2 more
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A novel trajectory learning method for robotic arms based on Gaussian Mixture Model and k-value selection algorithm. [PDF]
PLoS ONEIn the field of robotic arm trajectory imitation learning, Gaussian Mixture Models are widely used for their ability to capture the characteristics of complex trajectories. However, one major challenge in utilizing these models lies in the initialization
Jingnan Yan +4 more
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The Infinite Gaussian Mixture Model
, 2000 In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the "right" number of
Rasmussen, Carl Edward +2 more
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Intrinsically Interpretable Gaussian Mixture Model
Information, 2023 Understanding the reasoning behind a predictive model’s decision is an important and longstanding problem driven by ethical and legal considerations.
Nourah Alangari +3 more
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Deep Gaussian mixture models [PDF]
Statistics and Computing, 2017 Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, where, at each layer, the variables ...
Cinzia Viroli, Geoffrey J. McLachlan
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Gaussian Mixture Model for Marine Reverberations
Applied Sciences, 2023 Ocean reverberations, a significant interference source in active sonar, arise as a response generated by random scattering at the receiving end, a consequence of randomly distributed clutter or irregular interfaces. Statistical analysis of reverberation
Tongjing Sun +4 more
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