Results 211 to 220 of about 556,024 (263)

A Hybrid Soft Sensor Approach Combining Partial Least-Squares Regression and an Unscented Kalman Filter for State Estimation in Bioprocesses. [PDF]

Bioengineering (Basel)
Hermann L, Kremling A.
europepmc   +1 more source

A continuous kinematic calibration method for accuracy maintenance of industrial robot based on recursive least squares algorithm. [PDF]

Sci Rep
Qiao G, Jiang X, Zhang M, Gao C, Zhang Y.   +4 more
europepmc   +1 more source

R-based workflow to estimate chilling requirements in multiple fruit tree genotypes using Partial Least Squares regression: <i>Prunus armeniaca</i> L. case. [PDF]

MethodsX
Muñoz-Morales AM, Ortuño-Hernández G, Salazar JA, Martínez-Gómez P, Egea JA, Ruiz D, Delgado A.   +6 more
europepmc   +1 more source

A Minibatch Alternating Projections Algorithm for Robust and Efficient Magnitude Least-Squares RF Pulse Design in MRI. [PDF]

IEEE Trans Med Imaging
Martin JB, Sappo CR, Hardy BM, Grissom WA.   +3 more
europepmc   +1 more source

Mangrove Extraction Algorithm Based on Orthogonal Matching Filter-Weighted Least Squares. [PDF]

Sensors (Basel)
Li Y, Ma J, Fu D, Yuan J, Liu D.
europepmc   +1 more source

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Least squares estimates and the coverage of least squares costs

52nd IEEE Conference on Decision and Control, 2013
The least squares estimate xN minimizes the sum of the squared residuals equation over a finite set of observations (Ai, bi). At x = xN, the squared residuals ∥AixN-bi∥2 are called the “empirical costs”. Intuitively, the empirical costs carry information on the probability distribution of the cost ∥AxN-b∥2 that is paid for other, yet unseen, values of (
Carè, Algo, Garatti, Simone, Campi, Marco C.   +2 more
openaire   +3 more sources

Least-squares meshes

Proceedings Shape Modeling Applications, 2004., 2004
In this paper we introduce least-squares meshes: meshes with a prescribed connectivity that approximate a set of control points in a least-squares sense. The given mesh consists of a planar graph with arbitrary connectivity and a sparse set of control points with geometry. The geometry of the mesh is reconstructed by solving a sparse linear system. The
Olga Sorkine, Daniel Cohen-Or
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Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression

2012
Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table.
Hervé, Abdi, Lynne J, Williams
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The Inefficiency of Least Squares

Biometrika, 1975
SUMMARY Two criteria are set up to judge the relative performance of the least squares estimator and the best linear unbiased estimator of , in the linear model y = X/, + u, where E(u) = 0, E(uu') = F. The matrices X and r are found so that the relative performance of least squares is worst.
Bloomfield, Peter, Watson, Geoffrey S.
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The Method of Least Squares

2016
The English version of this paper appeared two years after the Chinese “original”. During the 1950s and early 1960s, DDK visited China several times on exchange programmes.
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