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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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Error‐bounded Image Triangulation
Computer Graphics Forum, 2023AbstractWe propose a novel image triangulation method to reduce the complexity of image triangulation under the color error‐bounded constraint and the triangle quality constraint. Meanwhile, we realize a variety of visual effects by supporting different types of triangles (e.g., linear or curved) and color approximation functions (e.g., constant ...
Zhi-duo Fang +3 more
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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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Bounds and error bounds for queueing networks
Annals of Operations Research, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Error Bound for Conic Inequality
Vietnam Journal of Mathematics, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zheng, Xi Yin, Ng, Kung Fu
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Error Bounds for Convex Polynomials
SIAM Journal on Optimization, 2009In this paper, the author establishes new properties of convex multivariate polynomials, using convex analysis. The author shows that for a convex polynomial \(f\) which is not constant on any affine subspace, if the lower level set of \(f\) (i.e., the set where \(f\) is nonpositive) is unbounded, then \(f\) can be represented as a sum of a convex ...
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2014
In Chap. 3, we discussed the main ideas of fully reliable error control methods and the corresponding numerical algorithms with the paradigm of simple elliptic type problems. This chapter is intended to show a deep connection between a posteriori estimates of the functional type and physical relations generating the problem.
Olli Mali +2 more
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In Chap. 3, we discussed the main ideas of fully reliable error control methods and the corresponding numerical algorithms with the paradigm of simple elliptic type problems. This chapter is intended to show a deep connection between a posteriori estimates of the functional type and physical relations generating the problem.
Olli Mali +2 more
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A Bound for Error-Correcting Codes
IBM Journal of Research and Development, 1960This paper gives two new bounds for the code word length n which is required to obtain a binary group code of order 2k with mutual distance d between code words. These bounds are compared with previously known bounds, and are shown to improve upon them for certain ranges of k and d. Values of k and d are given for which one of these bounds can actually
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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
We present a feedback motion planning algorithm, Bounded-Error LQR-Trees, that leverages reinforcement learning theory to find a policy with a bounded amount of error. The algorithm composes locally valid linear-quadratic regulators (LQR) into a nonlinear controller, similar to how LQR-Trees constructs its policy, but minimizes the cost of the ...
Barrett Ames, George Dimitri Konidaris
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We present a feedback motion planning algorithm, Bounded-Error LQR-Trees, that leverages reinforcement learning theory to find a policy with a bounded amount of error. The algorithm composes locally valid linear-quadratic regulators (LQR) into a nonlinear controller, similar to how LQR-Trees constructs its policy, but minimizes the cost of the ...
Barrett Ames, George Dimitri Konidaris
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Error bounds for correlation clustering
Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005This paper presents a learning theoretical analysis of correlation clustering (Bansal et al., 2002). In particular, we give bounds on the error with which correlation clustering recovers the correct partition in a planted partition model (Condon & Karp, 2001; McSherry, 2001).
Thorsten Joachims, John E. Hopcroft
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