simple least-squares approach
Least-Squares Policy Iteration
... new approach to reinforcement learning for control problems which com- bines value-function approximation with linear architectures and approximate policy iter- ...new approach is motivated by the ...
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A Least-squares Approach to Direct Importance Estimation
... We address the problem of estimating the ratio of two probability density functions, which is often referred to as the importance. The importance values can be used for various succeeding tasks such as covariate shift ...
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An alternative approach to approximating the moments of least squares estimators
... of least squares coefficient estimators in situations where endogene- ity and dynamics are ...a simple set of smoothness and moment conditions, can be applied to related ...
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Segmentation for path models and unobserved heterogeneity: The finite mixture partial least squares approach
... of simple strategies for escaping local optima include beginning the EM-algorithm from a wide range of (random) starting values or using clustering procedures, such as K-means, to obtain an initial partition of ...
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A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality
... standard least squares (LS) without using the known relation between the intermediate variable and the position ...SI approach, Chan and Ho have proposed [14] to use a two-stage WLS to solve for the ...
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Completely Recursive Least Squares and Its Applications
... An approach of joint state-and-parameter estimation, which is different from the state augmentation, is adopted, where the original nonlinear PE problem is reformulated as two loosely-coupled linear subproblems: ...
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Characterising the Human Auditory System using a Linear Least Squares System Identification Approach
... Disabling hearing loss affects many millions of people around the world. Early identification and suitable interventions, e.g., the provision of hearing aids, cochlear implants, etc., can help but are limited by the ...
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Some aspects of progeny testing Southdown rams : a thesis presented in part fulfilment of the requirements for the degree of Master of Agricultural Science in Massey University of Manawatu, New Zealand
... Least squares means, least squares deviations of sire groups from the means, least squares differences due to birthrank and sex, and partial regression coefficients for the characteristi[r] ...
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05_Linear_Regression_1.pdf
... Maximum Likelihood and Least Squares Geometry of Least Squares Sequential Learning Regularized Least Squares Multiple Outputs Loss Function for Regression The Bias-Variance Decomposition[r] ...
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Teaching Least Squares in Matrix Notation
... least squares (WLS), also called homoskedastic and heteroskedastic regressions, are the names used to distinguish the special case of equal values for all standard deviations from the case of different ...
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The critical success factors for organizational performance of SMEs in Malaysia: a partial least squares approach
... To investigate the relationship between critical success factors (Top Management Support, Customer Focus, Employee Orientation, Technology Orientation, and Entrepreneurial Orientatio[r] ...
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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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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 ...
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Optimal Dictionary for Least Squares Representation
... This simple example shows that given some statistical information about the random vectors to be represented, the question of designing a dictionary that minimizes the average cost of representation can be better ...
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Distributed Learning with Regularized Least Squares
... operator approach. Even for the classical least squares regularization scheme in the RKHS associated with a general kernel, we give the best learning rate in expectation in the ...
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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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On the computation of the structured total least squares estimator
... One approach, see References [11–13], to derive special purpose algorithms is to apply an iterative procedure, in which the constraint of (4) is linearized around the current approxima- tion point and an equality ...
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Linear least squares localization in sensor networks
... Localization in sensor networks is critical for search and rescue. Linear least squares (LLS) estimation is a sub-optimum but low-complexity localization algorithm based on measurements of location-related ...
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A note on approximating moments of least squares estimators
... The approach builds on Marriott and Pope (1954) and Kendall (1954), who consider the moments of the k-th order serial correlation coefficient in AR(1) ...the approach is applicable in principle to models ...
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Least squares regret and partially strategic players
... for least-squares ...behind least- squares ...Thus, least-squares regret should depend on the magnitudes of the regrets in one way or ...
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