Results 61 to 70 of about 14,024,872 (145)
Enhancing SPH using moving least-squares and radial basis functions [PDF]
, 2005 In this paper we consider two sources of enhancement for the meshfree Lagrangian particle method smoothed particle hydrodynamics (SPH) by improving the accuracy of the particle approximation.
R.A. Brownlee (7584983) +7 more
core
Local Regularization Assisted Orthogonal Least Squares Regression
, 2006 A locally regularized orthogonal least squares (LROLS) algorithm is proposed for constructing parsimonious or sparse regression models that generalize well.
Chen, S.
core +1 more source
Partial Least Squares Methods for Non-Metric Data [PDF]
, 2009 Partial Least Squares (PLS) methods embrace a suite of data analysis techniques based on algorithms belonging to PLS family. These algorithms consist in various extensions of the Nonlinear estimation by Iterative PArtial Least Squares (NIPALS) algorithm,
Russolillo, Giorgio
core +1 more source
, 2002 In a recent paper [4], Li et al. gave a generalized successive overrelaxation (GSCR) method for the least squares problems. In this paper, we show that the GSOR method can be applied to the equality constrained least squares (LSE) problems and the ...
Li ZJ(李长军), Evans, DJ
core
Robust estimation of the vector autoregressive model by a least trimmed squares procedure. [PDF]
The vector autoregressive model is very popular for modeling multiple time series. Estimation of its parameters is typically done by a least squares procedure.
Croux, Christophe, Joossens, Kristel
core
A robust partial least squares method with applications [PDF]
, 2007 Partial least squares regression (PLS) is a linear regression technique developed to relate many regressors to one or several response variables. Robust methods are introduced to reduce or remove the effect of outlying data points.
Romera, Rosario +2 more
core +1 more source
A note on least squares fitting of signal waveforms [PDF]
Signal waveforms are very fast dampening oscillatory time series composed of exponential functions. The regular least squares fitting techniques are often unstable when used to fit exponential functions to such signal waveforms since such functions are ...
Mishra, SK
core
Benchmarking least squares support vector machine classifiers. [PDF]
In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a ( convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function
Suykens, Johan +7 more
core
On the equivalence between Total Least Squares and Maximum Likelihood PCA
, 2005 The maximum likelihood PCA (MLPCA) method has been devised in chemometrics as a generalization of the well-known PCA method in order to derive consistent estimators in the presence of errors with known error distribution.
Wentzell, P. +3 more
core +1 more source
Robust estimation of the vector autoregressive model by a trimmed least squares procedure. [PDF]
The vector autoregressive model is very popular for modeling multiple time series. Estimation of its parameters is done by a least squares procedure. However, this estimation method is unreliable when outliers are present in the data, and there is a need
Croux, Christophe, Joossens, Kristel
core