Results 221 to 230 of about 862,081 (264)
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On Weak Residual Error Estimation
SIAM Journal on Scientific Computing, 1996The author develops a general framework for weak residual error estimators applied to various types of boundary value problems in connection with finite element and finite volume approximations. The paper illustrates basic ideas commonly shared by various applications in error estimation and adaptive computation. Some numerical results are given.
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Error estimation and control for ODEs
Journal of Scientific Computing, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Error estimation and error bounds for neural networks
Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, 2002A method is proposed to estimate the standard error of predicted values in multilayer perceptron (MLP). It is based on likelihood theory. It holds for all feedforward networks, irrespective of the topology or the specific task at hand. In addition, the bounds on a neural network with perturbed weights and inputs is analytically derived.
Hualou Liang, Guiliang Dai
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On Quasi-Norm Interpolation Error Estimation And A Posteriori Error Estimates for p-Laplacian
SIAM Journal on Numerical Analysis, 2002The paper is devoted to the finite element approximation of the \(p\)-Laplacian with zero Dirichlet data. The authors establish a series of interpolation error estimates for several widely used averaging interpolators in some quasi-norms. These estimates are among the key ingredients in their improved a posteriori error analysis for the \(p\)-Laplacian.
Wenbin Liu, Ningning Yan
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Generalization error of ensemble estimators
Proceedings of International Conference on Neural Networks (ICNN'96), 2002It has been empirically shown that a better estimate with less generalization error can be obtained by averaging outputs of multiple estimators. This paper presents an analytical result for the generalization error of ensemble estimators. First, we derive a general expression of the ensemble generalization error by using factors of interest (bias ...
Naonori Ueda, Ryohei Nakano
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Bounds and error estimates for radiosity
Proceedings of the 21st annual conference on Computer graphics and interactive techniques - SIGGRAPH '94, 1994We present a method for determining a posteriori bounds and estimates for local and total errors in radiosity solutions. The ability to obtain bounds and estimates for the total error is crucial fro reliably judging the acceptability of a solution. Realistic estimates of the local error improve the efficiency of adaptive radiosity algorithms, such as ...
Dani Lischinski +2 more
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2003
In this chapter we assume the spacetime K is foliated by a double null canonical foliation that satisfies the assumptions $$O \leqslant \epsilon_0 ,\,D \leqslant \epsilon_0 ,$$ (6.0.1) and we make use of the inequality proved in Theorem M7 $$R \leqslant cQ_K^{\frac{1} {2}} .$$ (6.0.2)
Sergiu Klainerman, Francesco Nicolò
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In this chapter we assume the spacetime K is foliated by a double null canonical foliation that satisfies the assumptions $$O \leqslant \epsilon_0 ,\,D \leqslant \epsilon_0 ,$$ (6.0.1) and we make use of the inequality proved in Theorem M7 $$R \leqslant cQ_K^{\frac{1} {2}} .$$ (6.0.2)
Sergiu Klainerman, Francesco Nicolò
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Guaranteed a-posteriori error estimation and a-posteriori estimation of the pollution error
2001Abstract In Chapter 5 we discussed various estimators for the energy norm of the error in the finite element solution. We discussed the Neumann element residual estimator, the subdo-main residual estimator, the explicit residual estimator, the recovery estimators (e.g. the ZZ–SPR estimator, etc), and analyzed them in two ways:
Ivo Babuška, Theofanis Strouboulis
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1998
In the conceptual idea described in Chapter 6.1, it was assumed that the mean of the sample would deviate from that of the population from which it was collected. This therefore raises the question of how “precisely” does the mean value of the sample reflect that of the population. In other words, how large is the uncertainty of the mean value, or what
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In the conceptual idea described in Chapter 6.1, it was assumed that the mean of the sample would deviate from that of the population from which it was collected. This therefore raises the question of how “precisely” does the mean value of the sample reflect that of the population. In other words, how large is the uncertainty of the mean value, or what
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