Gaussian Mixture Models

GPTKB entity

Statements (48)
Predicate Object
gptkbp:instance_of gptkb:Model
gptkbp:analyzes scatter plots
contour plots
3 D plots
gptkbp:application image processing
finance
bioinformatics
speech recognition
gptkbp:based_on Gaussian distributions
gptkbp:can_be subpopulations
gptkbp:can_be_extended_by Bayesian Gaussian Mixture Models
Hierarchical Gaussian Mixture Models
Variational Gaussian Mixture Models
gptkbp:can_create gptkb:probability_density_function
gptkbp:composed_of multiple Gaussian components
gptkbp:controls overlapping clusters
gptkbp:fit Expectation-Maximization algorithm
gptkbp:has_impact_on data is generated from a mixture of several distributions
https://www.w3.org/2000/01/rdf-schema#label Gaussian Mixture Models
gptkbp:is_compared_to hidden Markov models
k-means clustering
kernel density estimation
gptkbp:is_enhanced_by regularization techniques
gptkbp:is_evaluated_by Akaike Information Criterion
cross-validation
Bayesian Information Criterion
gptkbp:is_implemented_in gptkb:MATLAB
gptkb:Java
gptkb:R
gptkb:Library
gptkbp:is_optimized_for gradient descent
stochastic optimization
gptkbp:is_used_for clustering
model complex distributions
density estimation
identify subpopulations
gptkbp:is_used_in topic modeling
customer segmentation
anomaly detection
image segmentation
gptkbp:orbital_period mean
covariance
mixing coefficients
gptkbp:requires number of components
gptkbp:training labeled data
unlabeled data
gptkbp:bfsParent gptkb:Scikit-learn
gptkbp:bfsLayer 4