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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. These techniques make efficient use of a reduced number
Agostini, Alejandro Gabriel, Celaya Llover, Enric   +1 more
openaire   +3 more sources

Shape Representation using Gaussian Process mixture models [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Traditional explicit 3D representations, such as point clouds and meshes, demand significant storage to capture fine geometric details and require complex indexing systems for surface lookups, making functional representations an efficient, compact, and ...
P. Sapoutzoglou, G. Terzakis, G. Floros, M. Pateraki   +3 more
doaj   +1 more source

Kernel Density Estimators for Gaussian Mixture Models

Lithuanian Journal of Statistics, 2013
The problem of nonparametric estimation of probability density function is considered. The performance of kernel estimators based on various common kernels and a new kernel K (see (14)) with both fixed and adaptive smoothing bandwidth is compared in ...
Tomas Ruzgas, Indrė Drulytė
doaj   +1 more source

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
doaj   +1 more source

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, Remy Mutapay Tshimona, Pierre Kafunda Katalay, Simon Ntumba Badibanga, Eugène Mbuyi Mukendi   +4 more
doaj   +1 more source

Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering

Sensors, 2016
In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking.
Qian Zhang, Taek Lyul Song
doaj   +1 more source

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
openaire   +1 more source

Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry. [PDF]

PLoS ONE, 2015
Mixture - modeling of mass spectra is an approach with many potential applications including peak detection and quantification, smoothing, de-noising, feature extraction and spectral signal compression.
Andrzej Polanski, Michal Marczyk, Monika Pietrowska, Piotr Widlak, Joanna Polanska   +4 more
doaj   +1 more source

UNSUPERVISED CHANGE DETECTION IN SAR IMAGES USING GAUSSIAN MIXTURE MODELS [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
In this paper, we propose a method for unsupervised change detection in Remote Sensing Synthetic Aperture Radar (SAR) images. This method is based on the mixture modelling of the histogram of difference image.
E. Kiana, S. Homayouni, M. A. Sharifi, M. Farid-Rohani   +3 more
doaj   +1 more source

Characterizing the Conditional Galaxy Property Distribution Using Gaussian Mixture Models

The Astrophysical Journal, 2023
Line-intensity mapping (LIM) is a promising technique to constrain the global distribution of galaxy properties. To combine LIM experiments probing different tracers with traditional galaxy surveys and fully exploit the scientific potential of these ...
Yucheng Zhang, Anthony R. Pullen, Rachel S. Somerville, Patrick C. Breysse, John C. Forbes, Shengqi Yang, Yin Li, Abhishek S. Maniyar   +7 more
doaj   +1 more source