least-squares regression
Tree Based Orthogonal Least Squares Regression with Repeated Weighted Boosting Search
... Abstract—Orthogonal Least Squares Regression (OLSR) selects each regressor by repeated weighted boosting search ...sparser regression model in the framework of OLSR with ...each ...
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On the Consistency of Feature Selection using Greedy Least Squares Regression
... Moreover, if the eigenvalue ρ( F ¯ ) is bounded away from zero, then the greedy algorithm can reliably identify features as long as each nonzero coefficient is larger than a constant times the noise level. In comparison, ...
14
Derivative Estimation Based on Difference Sequence via Locally Weighted Least Squares Regression
... linear regression representation in which the derivative is just the intercept ...linear regression model via locally weighted least ...weighted least squares regression, the ...
25
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
... our least-squares regression framework, which is risk minimization with the square loss, together with the main assumptions regarding our model and our ...
51
Speech Emotion Recognition with MPCA and Kernel Partial Least Squares Regression
... Abstract—Speech signal is one of the major means for communication which carries not only semantic, but personal information , such as genders and emotions. The researches about speech emotion have become more and more ...
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Data-driven Calibration of Penalties for Least-Squares Regression
... the least-squares regression framework, without assuming a particular shape for the ...penalized least squares for Gaussian ho- moscedastic ...
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Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space
... The proposed kernel PLS uses the NIPALS procedure to iteratively estimate the desired components. We have already pointed out that the NIPALS algorithm is very similar to the power method and as with this method was ...
27
Local Regularization Assisted Orthogonal Least Squares Regression
... A basic principle in practical nonlinear data modeling is the parsimonious principle of ensuring the smallest possible model that explains the data. The orthogonal least squares (OLS) algorithm [9,11] is an ...
27
Orthogonal least squares regression with tunable kernels
... A basic principle in practical nonlinear data modelling is the parsimonious principle of ensuring the smallest possible model that explains the training data. The existing sparse kernel modelling methods [1]–[6] place ...
5
Determination of Hexazinone in Environmental Samples by Uv-Vis Spectrophotometry-Partial Least Squares Regression
... 15. Ferrand M., Huquet B., Barbey S., Barillet F., Faucon F., Larroque H., Leray O., Trommenschlager J.M. and Brochard M., Determination of fatty acid profile in cow's milk using mid-infrared spectrometry: Interest of ...
6
1. Simultaneous determination of drugs used for chronic active gastritis disease by chemometric methods
... component regression (PCR) and partial least squares regression (PLSR) were successfully performed to simultaneous determination of clarithromycin , amoxicillin and lansoprazole in a ...
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Handling Multicollinearity; A Comparative Study Of The Prediction Performance Of Some Methods Based On Some Probabiltiy Distributions
... partial least squares regression, principal component regression, ridge regression and multiple linear regression methods in modeling and predicting daily mean PM10 ...
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RuitingYang_MSIS_Paper_finalVersion.pdf
... ordinary least squares regression, ridge regression, and LASSO regression models perform well, and predictors such as a talk’s number of language translations, average Internet ...
63
A Comparative Study On Some Methods For Handling Multicollinearity Problems
... In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory ...in regression analysis. ...
11
On the ease of predicting the thermodynamic properties of beta-cyclodextrin inclusion complexes
... statistical regression methods, namely principal components regression (PCR) [14], partial least squares regression (PLSR) [14] and support vector regression with forward feature ...
11
Functional Analysis of Chemometric Data
... by least squares approximation (spline regression) on the observ- ed absorbance at different wavelengths ...Second, least squares estimation of the parameters of model ...partial ...
10
A Risk Comparison of Ordinary Least Squares vs Ridge Regression
... ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a principal component analysis) and then ...
7
Predicting the quality of ryegrass using hyperspectral imaging
... This study examines the utility of Hyperspectral Imag- ing (HSI) based methods for non-invasive assessment of the composition of ryegrass. The quality of forage is an important component of animal performance and envi- ...
12
Stabilized Least Squares Migration
... “least squares migration”. Least squares migration (LSM) is an attempt to reconstruct the true inverse solution to the wave equation, rather than its adjoint solution, which is the traditional ...
148
A Comparison Study of Ridge Regression and Principle Component Regression with Application
... ridge regression (RR), principle component regression (PCR), and Latent Root LR ...the regression coefficients by ridge regression (RR) and principle component regression ...partial ...
11