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
|