Results 261 to 270 of about 655,974 (311)

An Error Estimate for CVA

Key Engineering Materials, 2001
The Canonical Variate Analysis has been applied in many circumstances as a powerful dynamic identification tool. Its capability of overcoming the high modal density matter is also improved by implemented special tools such as Probability Density Function and Modal Assurance Criterion stabilisation, making the CVA technique very reliable to monitor ...
FASANA, ALESSANDRO, GARIBALDI, Luigi, MARCHESIELLO, STEFANO, S. SORRENTINO   +3 more
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Bootstrap Techniques for Error Estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987
The design of a pattern recognition system requires careful attention to error estimation. The error rate is the most important descriptor of a classifier's performance. The commonly used estimates of error rate are based on the holdout method, the resubstitution method, and the leave-one-out method.
Anil K. Jain 0001, Richard C. Dubes, Chaur-Chin Chen   +2 more
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Error Estimation for Nordsieck Methods

Numerical Algorithms, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
John C. Butcher, Zdzislaw Jackiewicz
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Channel and capacity estimation errors

IEEE Communications Letters, 2002
Systems with multiple element transmitter and receiver arrays have been shown to achieve very high spectral efficiencies. The theoretically achievable Shannon capacity is a function of the channel between the transmitters and the receivers. On the simulation level, one assumes certain statistical characteristics for the channel, but on a practical ...
Kyritsi, Persefoni, Cox, D.C., Valenzuela, R.A.   +2 more
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On the estimation of probability of error

1974 IEEE Conference on Decision and Control including the 13th Symposium on Adaptive Processes, 1974
This paper considers the problem of estimation of classification error in Pattern Recognition. A Theorem is presented to obtain the changes in the eigenvalues and eigenvectors of matrices of the form S2 -1 S1, when there are changes of first order of smallness in the real Symmetric matrices Si, i=1, 2.
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On Weak Residual Error Estimation

SIAM Journal on Scientific Computing, 1996
The 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, 2005
zbMATH 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, 2002
A 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, 2002
The 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), 2002
It 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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