Moving Least Squares (MLS)
A New Technique for Image Zooming Based on the Moving Least Squares
... Moving Least Squares (MLS), originated by mathematicians for data fitting and surface construction, can be categorized as a method of finite series representation of ...
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Convergence rate for the moving least squares learning with dependent sampling
... the moving least-squares (MLS) method by the regression learning framework under the assumption that the sampling process satisfies the α -mixing ...
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The use of moving least squares for a smooth approximation of sampled data
... As an alternative, the moving least-squares approximation method is presented. The method enables a smooth and stable approximation over arbitrarily large domains. No particular partition o f the dom ...
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Moving Least Squares and Gauss Legendre for Solving the Integral Equations of the Second Kind
... the moving least squares method. The moving least squares methodology is an effective technique for the approximation of an unknown function by using a set of disordered ...
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3D Deformation Using Moving Least Squares
... We present a 3d deformation method based on Moving Least Squares that extends the work by Schaefer et al. [Schaefer et al. 2006] to the 3d setting. The user controls the deformation by ma- nipulating ...
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Matrices Associated with Moving Least Squares Approximation and Corresponding Inequalities
... Matrices Associated with Moving Least-Squares Approximation and Corresponding Inequalities Svetoslav Nenov, Tsvetelin Tsvetkov Department of Mathematics, University of Chemical Technolog[r] ...
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Approximation Effects Due to Diffuse Derivatives from Polynomial Basis
... Abstract— Moving Least Squares(MLS) is a method of reconstructing functions that are continuous from a group of random point samples through the calculation of a weighted least squares ...
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One-dimensional modeling of aquifer contamination using a meshless method
... Moving Least Squares (MLS) approximation For the first time in 1992, Nirvelles and colleagues presented the moving least squares approximation to generate form ...
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RPIM and RPIM-MLS based MDLSM method for the solution of elasticity problems
... Furthermore, a number of robust, adaptive renement techniques have been recently presented in order to improve the eciency of the MDLSM method [13-15]. The original DLSM and MDLSM method use the Moving ...
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A Collocation Method with Modified Equilibrium on Line Method for Imposition of Neumann and Robin Boundary Conditions in Acoustics (TECHNICAL NOTE)
... In this paper, a collocation method with the modified ELM is applied to solve the two- dimensional acoustical problems. The performance of the modified ELM is studied for collocation methods based on two different ...
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Coupling of adaptive refinement with variational multiscale element free Galerkin method for high gradient problems
... The meshfree methods [1,2,3,4,5,6,7,8,9] have been proposed as an alternative numerical techniques to the FEM. This class of numerical methods solve the problem through constructing the function based on a series of ...
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[13] . In the present paper, the collocation based
... using moving least squares shape function obtains best approximation for scatter nodes, there are some well-known disadvantages such as complex computations, lack the Kronecker delta function and the ...
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Teaching Least Squares in Matrix Notation
... generalized least squares can be thought at the second year undergraduate with reasonable appreciation from the ...the least squares allows to easily retrieve the results obtainable with ...
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Least squares approximations of power series
... In this paper we obtain analogs to (1.4) and (1.6) for power series f defined on the open interval ( − 1, 1). Such functions f (especially without closed forms) arise, for ex- ample, in solutions to differential ...
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Optimal Dictionary for Least Squares Representation
... The problem addressed in this article differs from the mainstream research of finding dictionaries offering sparse ( ` 0 -optimal) representations in the sense that our objective is to f[r] ...
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A novel interpretation of least squares solution
... We show that the well-known least squares LS solution of an overdetermined system of linear equations is a convex combination of all the non-trivial solutions weighed by the squares of t[r] ...
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Image magnification by least squares surfaces
... This paper continues as follows. In the second part, quadratic surfaces and the theory of least squares will be discussed. In the third part, the least square planes, suggested algorithms, and ...
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RLScore: Regularized Least-Squares Learners
... RLScore is a Python open source module for kernel based machine learning. The library provides implementations of several regularized least-squares (RLS) type of learners. RLS methods for regression and ...
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Distributed Learning with Regularized Least Squares
... Here we only consider distributed learning with the regularized least squares. It would be of great interest and value to develop the theory for distributed learning with other algorithms such as spectral ...
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On the computation of the structured total least squares estimator
... Unlike the methods mentioned above, which solve the STLS problem in its original for- mulation (4), the proposed methods solve an equivalent optimization problem, derived by analytically minimizing (4) over p , for a xed ...
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