Results 71 to 80 of about 418,071 (190)
Precautionary Learning and Inflationary Biases [PDF]
Recursive least squares learning is a central concept employed in selecting amongst competing outcomes of dynamic stochastic economic models. In employing least squares estimators, such learning relies on the assumption of a symmetric loss function ...
Dave, Chetan, Feigenbaum, James
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Kernel Recursive Least Squares Function Approximation in Game Theory Based Control
Procedia Technology, 2016 AbstractA game theoretic aspect in reinforcement learning based controller design with kernel recursive least squares algorithm for value function approximation is proposed in this paper. A kernel recursive least-squares-support vector machine is used to realize a mapping from state, controller's action and disturber's action to Q-value function ...
Shah, Hitesh, Gopal, M.
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Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) [PDF]
, 2013 We consider the stochastic approximation problem where a convex function has to be minimized, given only the knowledge of unbiased estimates of its gradients at certain points, a framework which includes machine learning methods based on the minimization
Bach, Francis, Moulines, Eric
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Smoothing Approximations for Least Squares Minimization with L1-Norm Regularization Functional
International Journal of Analysis and Applications, 2021 The paper considers the problem of least squares minimization with L1-norm regularization functional. It investigates various smoothing approximations for the L1-norm functional. It considers Quadratic, Sigmoid and Cubic Hermite functionals. A Tikhonov regularization is then applied to each of the resulting smooth least squares minimization problem ...
Henrietta Nkansah +2 more
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AppliedMathThis work presents the Leal method for the approximation of integrals without known exact solutions, capable of multi-expanding simultaneously at different points.
Hector Vazquez-Leal +5 more
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Preconditioning of Radial Basis Function Interpolation Systems via Accelerated Iterated Approximate Moving Least Squares Approximation [PDF]
, 2009 The standard approach to the solution of the radial basis function interpo- lation problem has been recognized as an ill-conditioned problem for many years. This is especially true when infinitely smooth basic functions such as multiquadrics or Gaussians are used with extreme values of their associated shape parameters.
Gregory E. Fasshauer, Jack G. Zhang
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Least squares approximations [PDF]
, 1962 Thesis (M.A.)--Boston UniversityThis paper, utilizing the properties of Vector spaces, describes an approach to polynomial approximations of functions defined analytically or by a set of observations over some interval.
Wiener, Marvin
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Frontiers in Applied Mathematics and StatisticsTo address the issue that polynomial approximation methods strongly depend on the analytical form of the objective function, this study proposes a new minimax polynomial approximation method based on the Chebyshev equioscillation theorem.
Letong Zhou, Liguo Zhao
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Sensors, 2011 The star centroid estimation is the most important operation, which directly affects the precision of attitude determination for star sensors. This paper presents a theoretical study of the systematic error introduced by the star centroid estimation ...
Jingyan Song +3 more
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Benchmarking least squares support vector machine classifiers. [PDF]
In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a ( convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function
Baesens, Bart +7 more
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