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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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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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Local error estimates in quadrature
BIT, 1991zbMATH Open Web Interface contents unavailable due to conflicting licenses.
ROMANI, FRANCESCO, Favati P, Lotti G.
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Microstructural Decomposition Error Estimates
ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 1999AbstractComputational simulations of interacting microstructure in solid structures, with methods such as the finite element method, require solutions to numerically enormous boundary value problems. The primary objective of this work is to introduce a‐posteriori error bounds for a domain decomposition which can be used to reduce the computational ...
Zohdi, T., Wriggers, P.
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Error Estimation for the Particle Filter
2019 53rd Annual Conference on Information Sciences and Systems (CISS), 2019The particle filter is a popular algorithm for solving the state-space problem for its easy implement. Many previous studies have been conducted to study the asymptotical behavior of particle filters. In our previous works, we divided the error of particle filter into two parts.
Ziyu Liu 0001, James C. Spall
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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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Estimating the Error in the Koebe Construction
Computational Methods and Function Theory, 2012\textit{P. Koebe} [``Über eine neue Methode der konformen Abbildung und Uniformisierung'', Gött. Nachr. 1912, 861--878 (1912; JFM 43.0520.01)] proposed an iterative method, the so-called Koebe construction, for approximating the unique conformal map \(f\) of a nondegenerate \(n\)-connected domain \(D\) with \(\infty\in D\) and \(0\not\in D\) onto a ...
Andreev, Valentin V. +1 more
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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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