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In particle physics, CLs [1] represents a statistical method for setting upper limits (also called exclusion limits [2]) on model parameters, a particular form of interval estimation used for parameters that can take only non-negative values.
In order to ensure efficient estimation and prediction performance of PCR as an estimator of , Park (1981) [4] proposes the following guideline for selecting the principal components to be used for regression: Drop the principal component if and only if < /.
Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested.
For most systems the expectation function {() ()} must be approximated. This can be done with the following unbiased estimator ^ {() ()} = = () where indicates the number of samples we use for that estimate.
Partial least squares (PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression [1]; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space of maximum ...
Bootstrap aggregating, also called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy of ML classification and regression algorithms. It also reduces variance and overfitting.
Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need to be tuned.
The lattice recursive least squares adaptive filter is related to the standard RLS except that it requires fewer arithmetic operations (order N). [4] It offers additional advantages over conventional LMS algorithms such as faster convergence rates, modular structure, and insensitivity to variations in eigenvalue spread of the input correlation ...