the least squares
The Dual of the Least Squares Method
... the least-squares ...the least-squares method, may be very useful in all problems of physics and astronomy where one needs to obtain the most precise results possible from ...
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Distributed Learning with Regularized Least Squares
... the least squares regularization scheme in a reproducing kernel Hilbert space ...the least squares regularization scheme to each data subset to produce an output function, and then takes an ...
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On weighted structured total least squares
... is a solution technique for an overdetermined system of equations AX ≈ B, A ∈ IR m × n , B ∈ IR m × d . It is a natural generalization of the least squares approximation method when the data in both A and B ...
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On the computation of the structured total least squares estimator
... We consider numerical methods for the solution of the optimization problem (5). One approach is to use standard algorithms for local optimization. The choice of the optimization method is inspired by the need to use as ...
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AIC under the framework of least squares estimation
... is parameter estimation in the framework of a least squares estimation problem ([10] and all the references herein). As such, our goal in this paper is to provide a concise formulation for the AIC using the ...
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Deformation analysis with Total Least Squares
... a Least Squares (LS) technique is used for the transforma- tion ...Total Least Squares (TLS) that is considerably a new approach in geodetic ...the Least Squares (LS) and the ...
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Image magnification by least squares surfaces
... In this paper, a new method of the least squares surface has been used to enlarge images. Despite simple implementation and low computational com- plexity, this method provides more satisfactory results ...
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Download Download PDF
... process on unit sphere, and applied that to derive covariance functions related to the earth's disturbing potential. Moritz [5] made a systematic and comprehensive presentation of the theory of least ...
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Least squares estimation of a shift in linear processes
... the least squares method (LS) to the estimation of a shift ...The least squares procedure also allows a broader specification of correlation structure in the data than MLE can typically ...
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Teaching Least Squares in Matrix Notation
... The least squares method, a fundamental piece of knowledge for students of all scientific tracks, is often introduced considering the simple linear regression with only two parameters to be ...the ...
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Overview of total least squares methods
... alternating least squares ...total least squares problems and is globally convergent, with linear convergence ...The least squares nature of the problem is not exploited by the ...
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Fitting Data with Different Error Models »
... It can be seen that (in the case of Gaussian-type measurement noise) only the type of the error model determines the parameter values, since we should always minimize the least squares of the errors. There ...
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Least-Squares Policy Iteration
... LSPI was implemented 9 using a combination of Matlab and C and was tested on the following problems: chain walk, inverted pendulum balancing, and bicycle balancing and riding. The chain walk class of problems has no ...
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The element wise weighted total least squares problem
... The parameter estimation problem is typically defined as an optimization problem: an appropriate cost function depending on the data is minimized over the estimated param- eters. The classical approach, the ...
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Least squares regret and partially strategic players
... apply least-squares regret to a number of well-known games, in particular, the Dollar Auction; Bertrand competition; inspection games; Matching Pennies; Chicken; coordination games; Battle of the Sexes; and ...
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Linear least squares localization in sensor networks
... In order to avoid the explicit minimization problem and obtain a closed form solution, the nonlinear expres- sions of observations can be linearized using the linear least squares (LLS) algorithms. ...
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Kernel Partial Least Squares for Stationary Data
... partial least squares algorithm is competitive with other regression methods such as ridge regression and principal component regression, needing generally fewer iterations than the latter to achieve ...
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Completely Recursive Least Squares and Its Applications
... Most power system state estimation (SE) programs are formulated as static weighted-least-squares (WLS) problems with one-scan data [Monticelli00]. Dynamic state estimation (DSE) is not popularly applied due ...
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A note on approximating moments of least squares estimators
... We present results to facilitate the asymptotic approximation of the mo- ments of least squares coefficient estimators under similar assumptions to Phillips (2000), but focussing on the OLS estimator. The ...
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