Add to ORKGWe propose an Gaussian Mixture Model (GMM) learning algorithm, based on our previous work of GMM expansion idea. The new algorithm brings more robustness and simplicity than classic Expectation Maximization (EM) algorithm. It also improves the accuracy and only take 1 iteration for learning. We theoretically proof that this new algorithm is guarantee to converge regardless the parameters initialisation. We compare our GMM expansion method with classic probability layers in neural network leads to demonstrably better capability to overcome data uncertainty and inverse problem. Finally, we test GMM based generator which shows a potential to build further application that able to utilized distribution random sampling for stochastic variation a...
A Bayesian SOM (BSOM) [8], is proposed and applied to the unsupervised learning of Gaussian mixture ...
For the Gaussian mixture learning, the expectation-maximization (EM) algorithm as well as its modifi...
The central focus of this work is the Gaussian Mixture Model (GMM), a machine learning model widely ...
We build up the mathematical connection between the "Expectation-Maximization" (EM) algori...
peer reviewedLearning a Gaussian mixture with a local algorithm like EM can be difficult because (i)...
Gaussian Mixture Models (GMM) are one of the most potent parametric density estimators based on the ...
This paper uses Gaussian mixture model instead of linear Gaussian model to fit the distribution of e...
Gaussian mixture model (GMM) has been widely used in fields of image processing and investment data ...
Unsupervised learning has been widely used in many real-world applications. One of the simplest and ...
The learning of variational inference can be widely seen as first estimating the class assignment va...
University of AmsterdamWe present a deterministic greedy method to learn a mixture of Gaussians whic...
peer reviewedOnline learning, Gaussian mixture model, Uncertain model. We present a method for incre...
peer reviewedThis article concerns the greedy learning of gaussian mixtures. In the greedy approach,...
Recent approaches to Reinforcement Learning (RL) with function approximation include Neural Fitted Q...
Gaussian mixture models are often used for probability density estimation in pattern recognition and...
A Bayesian SOM (BSOM) [8], is proposed and applied to the unsupervised learning of Gaussian mixture ...
For the Gaussian mixture learning, the expectation-maximization (EM) algorithm as well as its modifi...
The central focus of this work is the Gaussian Mixture Model (GMM), a machine learning model widely ...
We build up the mathematical connection between the "Expectation-Maximization" (EM) algori...
peer reviewedLearning a Gaussian mixture with a local algorithm like EM can be difficult because (i)...
Gaussian Mixture Models (GMM) are one of the most potent parametric density estimators based on the ...
This paper uses Gaussian mixture model instead of linear Gaussian model to fit the distribution of e...
Gaussian mixture model (GMM) has been widely used in fields of image processing and investment data ...
Unsupervised learning has been widely used in many real-world applications. One of the simplest and ...
The learning of variational inference can be widely seen as first estimating the class assignment va...
University of AmsterdamWe present a deterministic greedy method to learn a mixture of Gaussians whic...
peer reviewedOnline learning, Gaussian mixture model, Uncertain model. We present a method for incre...
peer reviewedThis article concerns the greedy learning of gaussian mixtures. In the greedy approach,...
Recent approaches to Reinforcement Learning (RL) with function approximation include Neural Fitted Q...
Gaussian mixture models are often used for probability density estimation in pattern recognition and...
A Bayesian SOM (BSOM) [8], is proposed and applied to the unsupervised learning of Gaussian mixture ...
For the Gaussian mixture learning, the expectation-maximization (EM) algorithm as well as its modifi...
The central focus of this work is the Gaussian Mixture Model (GMM), a machine learning model widely ...