regularized-least-squares method
A fast regularized least-squares method for retinal vascular oxygen tension estimation using a phosphorescence lifetime imaging model
... . The choice of this size of the window was inspired by the fact that variation among oxygen tension values in a neighborhood of this size of window should physio- logically be negligible [15]. The regularization term in ...
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Input force estimation accounting for modeling errors and noise in responses
... estimation method based on Tikhonov-like total least squares method is ...This method can deal with the dynamic force estimation problem in which the measured vibration responses and ...
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GBP/USD Currency Exchange Rate Time Series Forecasting Using Regularized Least-Squares Regression Method
... Sfetsos and Siriopoulos[6] compare four methods including random walk (RW), linear regression (LR), auto regression integrated moving average (ARIMA), and artificial neural network (ANN) in forecasting exchange rate ...
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Regularized Least Squares Estimating Sensitivity for Self-calibrating Parallel Imaging
... As mentioned earlier, system of Eq. 7 is underdetermined and only the “low-resolution” of SoS reconstruction image is used to calculate the sensitivity profile, so that the reconstruction image is not necessarily ...
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RLScore: Regularized Least-Squares Learners
... The method produced the winning submission of sub-challenge 3 of 2014 Broad-DREAM Gene Essentiality Prediction Challenge due to its ability to select a minimal accurate subset of features for multi-task learning ...
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MKRMDA: multiple kernel learning based Kronecker regularized least squares for MiRNA–disease association prediction
... In fact, based on the hypothesis that functionally similar miRNAs are often associated with phenotypi- cally similar diseases and vice versa [12, 33–37], many computational models have been proposed for predict- ing ...
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Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
... learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key ...
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Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D optimality experimental design
... locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental ...LROLS method alone is capable of producing a very parsimonious model with excellent ...
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Regularized Nonlinear Least Trimmed Squares Estimator in the Presence of Multicollinearity and Outliers
... regularization method, has been es- tablished to encourage grouping effect when predictors are highly correlated and also useful when there exists a large number of predictors than that of the observations in ...
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II. PAST RLS ESTIMATION
... of least squares estimation method was proposed earlier in order to improve estimation of oxygen tension in retinal vessels and it was shown to be very ...the regularized least ...
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Sparse multi output radial basis function network construction using combined locally regularised orthogonal least square and D Optimality experimental design
... locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental ...LROLS method alone is capable of producing a very parsimonious RBF network model with ...
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Recovery of a Compressed Sensing CT Image Using a Smooth Re-weighted Function- Regularized Least-Squares Algorithm
... ery method for compressed sensing CT ...recovery method of compressed sensing sig- nal based on regularized smooth convex ...our method has a sound recovery effect and is well suitable for ...
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Distributed Learning with Regularized Least Squares
... Another method proposed recently is distributed learning based on a divide-and-conquer approach and a particular learning algorithm imple- mented in individual machines (Zhang et ...
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Least Squares Method from the View Point of Deep Learning
... Deep Learning may be stated as a successive learning method based on the least squares method. Therefore, to reconsider it from the view point of Deep Learning is very natural and we carry out ...
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L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs
... The least squares LASSO algorithm has been used frequently in practice to perform variable selection for analyzing genomic data (Cantor et al., 2010; Zhao et al., 2011; Wang et al., 2013, e.g.). However, ...
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A Least Squares Finite Element Method for the Extended Maxwell System
... Galerkin’s method requires specialized basis functions, usually the N´ed´elec’s curl conforming functions, which are hard to come by in high orders, the arising system of linear equations is never ...
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Optimization Method of Power Allocation in OFDM Using Least Squares Method
... Users are handled during a Round-Robin fashion and therefore the best free resource is tentatively allotted to this user. Since the simplest resource is picked 1st, the signal-to-noise reduces for every further ...
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Scattered data fitting using least squares with interpolation method
... Example 4.2 We consider the shape design of a car. For example, the hood (a part of a car). A set of data are given in Fig. 3 which are used to construct the hood. The data can be divided into two groups. One is marked ...
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ONLINE NON-NEGATIVE MATRIX FACTORIZATION FOR CLUSTERING OF NEWS DOCUMENT
... Online NMF (Non-negative matrix factorization) is a recently developed method for real time data analysis in an online context. Non-negative matrix factorization in the past has been used for static data analysis ...
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Complete Least Squares: A New Variable Screening and Selection Method.
... the oracle model; that is, we fit the true model using OLS and compute its ME. Figure 4.9 shows the ratio of the mean ME from the oracle fit to that of each procedure, along with its 95% confidence interval. Larger ...
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