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463
Leastsquares methods in reinforcement learning for control
 In SETN ’02: Proceedings of the Second Hellenic Conference on AI
, 2002
"... Abstract. Leastsquares methods have been successfully used for prediction problems in the context of reinforcement learning, but little has been done in extending these methods to control problems. This paper presents an overview of our research efforts in using leastsquares techniques for control ..."
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Cited by 25 (1 self)
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Abstract. Leastsquares methods have been successfully used for prediction problems in the context of reinforcement learning, but little has been done in extending these methods to control problems. This paper presents an overview of our research efforts in using leastsquares techniques
LeastSquares Congealing for Large Numbers of Images
"... In this paper we pursue the task of aligning an ensemble of images in an unsupervised manner. This task has been commonly referred to as “congealing ” in literature. A form of congealing, using a leastsquares criteria, has been recently demonstrated to have desirable properties over conventional co ..."
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Cited by 11 (2 self)
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In this paper we pursue the task of aligning an ensemble of images in an unsupervised manner. This task has been commonly referred to as “congealing ” in literature. A form of congealing, using a leastsquares criteria, has been recently demonstrated to have desirable properties over conventional
SUPERCONVERGENCE OF LEASTSQUARES MIXED FINITE ELEMENTS
"... Abstract. In this paper we consider superconvergence and supercloseness in the leastsquares mixed finite element method for elliptic problems. The supercloseness is with respect to the standard and mixed finite element approximations of the same elliptic problem, and does not depend on the proper ..."
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Cited by 3 (0 self)
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to prove the coercivity of the leastsquares mixed bilinear form in a straightforward manner. Using the same inequality, it can moreover be shown that the leastsquares mixed finite element linear system of equations can basically be solved with one single iteration step of the Block Jacobi method.
Past Input Reconstruction in Fast LeastSquares Algorithms
, 1997
"... ... computed variables in a fast leastsquares prediction algorithm, determine all past input sequences that would have given rise to the variables in question. This problem is motivated by the backward consistency approach to numerical stability in this algorithm class; the set of reachable variabl ..."
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Cited by 1 (0 self)
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as the impulse response of an appropriately constrained orthogonal filter, whose rotation parameters derive in a direct manner from the computed variables in a fast leastsquares prediction algorithm. Formulas showing explicitly the form of all valid past inputs should facilitate the study of what past input
Evolutionary Optimization of LeastSquares Support Vector Machines
"... Abstract The performance of Kernel Machines depends to a large extent on its kernel function and hyperparameters. Selecting these is traditionally done using intuition or a costly “trialanderror ” approach, which typically prevents these methods from being used to their fullest extent. Therefore, ..."
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, two automated approaches are presented for the selection of a suitable kernel function and optimal hyperparameters for the LeastSquares Support Vector Machine. The first approach uses Evolution Strategies, Genetic Algorithms, and Genetic Algorithms with floating point representation to find optimal
NONLINEAR LEASTSQUARES FILTERING AND FREQUENCY MODULATION
, 1960
"... requirements for This thesis concerns the use of optimum nonlinear filtering for the recovery of messages from modulated signals in the presence of additive Gaussian noise. Signaltonoise ratio, defined in a manner especially suited to nonlinear filtering, is taken as the measure of performance. Th ..."
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Cited by 1 (0 self)
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requirements for This thesis concerns the use of optimum nonlinear filtering for the recovery of messages from modulated signals in the presence of additive Gaussian noise. Signaltonoise ratio, defined in a manner especially suited to nonlinear filtering, is taken as the measure of performance
An efficient generalpurpose leastsquares refinement program for macromolecular structures
 Acta Cryst
, 1987
"... A package of programs has been developed for efficient restrained leastsquares refinement of macromolecular crystal structures. The package has been designed to be as flexible and general purpose as possible. The process of refinement is divided into basic units and an independent computer program ..."
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Cited by 46 (6 self)
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A package of programs has been developed for efficient restrained leastsquares refinement of macromolecular crystal structures. The package has been designed to be as flexible and general purpose as possible. The process of refinement is divided into basic units and an independent computer program
Consensusbased distributed sensor calibration and leastsquare parameter identification in wsns
 International Journal of Robust and Nonlinear Control
"... In this paper we study the problem of estimating the channel parameters for a generic wireless sensor network (WSN) in a completely distributed manner, using consensus algorithms. Specifically, we first propose a distributed strategy to minimize the effects of unknown constant offsets in the reading ..."
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Cited by 25 (8 self)
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in the reading of the Radio Strength Signal Indicator (RSSI) due to uncalibrated sensors. Then we show how the computation of the optimal wireless channels parameters, which are the solution of a global leastsquare optimization problem, can be obtained with a consensusbased algorithm. The proposed algorithms
Superconvergent Patch Recovery based on Polynomials Satisfying Interior Equilibrium
"... INTRODUCTION In the socalled superconvergent patch recovery (SPR) method [4], the recovered stress field around a FE node is written oe (x; y) = P(x; y) a , where P is a matrix of polynomial terms in some Cartesian coordinates (x; y) on a patch of elements surrounding the node, and a is a ..."
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vector of unknown coefficients. The SPR approach is characterized by requiring that this polynomial expansion fits locally the superconvergent points (also known as Barlow points [1]) of the elements in the patch in a least square manner. To increase the quality of the recovered stress field, the method
Weighted leastsquares finite elements based on Particle Imaging Velocimetry data
 J. Comput. Phys
"... Abstract The solution of the NavierStokes equations requires that data about the solution is available along the boundary. In some situations, such as particle imaging velocimetry, there is additional data available along a single plane within the domain, and there is a desire to also incorporate ..."
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Cited by 7 (2 self)
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. For addressing this problem, we examine the potential of leastsquares finite element methods (LSFEM) because of their flexibility in the enforcement of various boundary conditions. Further, by weighting the boundary conditions in a manner that properly reflects the accuracy with which the boundary values
Results 1  10
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