least-squares approximation method
The use of moving least squares for a smooth approximation of sampled data
... moving least-squares approximation method is ...The method enables a smooth and stable approximation over arbitrarily large ...the method is given in Section 1. The ...
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Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares
... unconstrained least-squares but also more gener- ally for a broad class of constrained ...original least-squares estimator in the regime m ...alternative method—known as the iterative ...
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Peak Estimation of a Spectrum from Noisy Measurements by Least Squares Piecewise Monotonic Data Approximation
... In this section we present an example of our method intended to illustrate the estimation of peaks in a Raman spectrum sample. As was noted already, the location of peaks and their intensities for the Raman ...
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An Example of Peak Finding in Univariate Data by Least Squares Approximation and Restrictions on the Signs of the First Differences
... the least squares piecewise monotonic data approximation method to the problem of locating significant extrema in univariate observations that are contaminated by random ...monotonic ...
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Iterative least squares method for global positioning system
... presented method is very efficient for the implemen- tation of the standard triangulation method based on non- linear ...LS method is required for positioning which leads to further reduction of the ...
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New Possibilities and Applications of the Least Squares Collocation Method
... the boundary ∂Ω. The above feature gives an opportunity to avoid the irremovable error related to shifting points away from boundary. Obviously, working with variously shaped cells complicates the algorithm for the ...
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Study the Behavior of Total Least Squares Technique in Adjusting GPS Field Data - A Case Study
... Least Squares (LS) is the most commonly used adjustment method in ...them; approximation of the surfaces in engineering structures [1], finding the relationship between global and Cartesian ...
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Least-Squares Policy Iteration
... value-function approximation with linear architectures and approximate policy iter- ...the least-squares temporal-difference learning algorithm (LSTD) for prediction problems, which is known for its ...
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Approximation Effects Due to Diffuse Derivatives from Polynomial Basis
... The approximation method called MLS Approximation is actually an alternative to the radial basis function ...moving least squares, let’s see a gist of exactly what a weighted ...
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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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A Least Squares Finite Element Method for the Extended Maxwell System
... It should be noted that the approximation in the Lemma 1 is probably not optimal as it only takes into account first order polynomials. Especially, in the Example 1, we obtained quite a good result with 5th order ...
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Scattered data fitting using least squares with interpolation method
... use least squares method ...traditional least squares method may lose accuracy at the points which are not ...called least squares with ...
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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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ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS
... Maguire, Pearson and Wynn (1952) studied mine accidents and showed that time intervals between industrial accidents follow exponential distribution. Cohen and Helm (1973) used (BLUE), (MLE), (ME), (MVUE) and MME to ...
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Partial Least Squares in Constructing Candidates Model Averaging Muhammad Arna Ramadhan, Bagus Sartono, Anang Kurnia
... Model averaging could be better choice in regression analysis when number of observations is much smaller the number of explanatory variables. Our method, PLSMA was developed to construct candidate models in model ...
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On the Equivalence of the Weighted Least Squares and the Generalised Least Squares Estimators, with Applications to Kernel Smoothing
... The paper establishes the conditions under which the generalised least squares estima- tor of the regression parameters is equivalent to the weighted least squares estimator. The equivalence ...
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The Dual of the Least Squares Method
... the method was made in the month of October, 1801, and the first clear night, when the planet was sought for* (by de Zach, December 7, 1801) as directed by the numbers deduced from it, restored the fugitive to ...
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Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method
... weak-constraint method. The regularization term added to the Gauss–Newton method, resulting in a glob- ally convergent Levenberg–Marquardt method, maintains the structure of the linearized ...
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The generalized frequency domain adaptive filtering algorithm as an approximation of the block recursive least squares algorithm
... The results of this experiment can be seen in Fig. 8, where λ = 0.99, μ = 1, K = 128, P = 128, Q = 256, and N = 64 have been chosen for all algorithms (if applicable). It can be seen that the RLS algorithm shows the best ...
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Stabilized Least Squares Migration
... The goal of LSM is to construct a “true amplitude” image (Plessix 2002), which isn’t possible in reality. In LSM we generate a solution which is theoretically devoid of errors in the reflectivity image, though in reality ...
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