Algorithms {prediction algorithms} can predict.
predictors
Factors, properties, or structures contribute to response values.
variable influence on prediction
Methods {variable influence on the prediction} can determine variable importance.
principal property
Variables {principal property} can be linear descriptor combinations.
non-parametric algorithms
Non-parametric algorithms can have alternating conditional expectations. Non-parametric methods {non-linear partial least-squares, vision} can find least squares.
outlier algorithms
Normal-distribution-outlier tests {Dixon's Q-test, vision} can measure ratio of smallest and largest differences.
Normal-distribution outlier tests {Grubbs' s-test, vision} can compare absolute values, of differences between mean and value, divided by standard deviation, to T distribution value.
network
Kohonen topology-preserving network mappings can retain topology. Topological indexes can represent graphs as numbers. Topological Tanimoto indexes can represent graphs as numbers.
rule induction system
IF/THEN statements {rule induction system, vision} can make output from input.
3-Computer Science-Systems-Computer Vision-Algorithms
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Description of Outline of Knowledge Database
Date Modified: 2022.0225