Results 241 to 250 of about 2,351,578 (288)
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Bootstrap Techniques for Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987The 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 +2 more
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Error Estimation for Nordsieck Methods
Numerical Algorithms, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
John C. Butcher, Zdzislaw Jackiewicz
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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, 1974This 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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Channel and capacity estimation errors
IEEE Communications Letters, 2002Systems 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 +2 more
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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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Estimation of the GSSM calibration error
Applied Optics, 2016The calibration of the tertiary mirror of the Thirty Meter Telescope, also known as the giant science steering mirror (GSSM), is a step of great significance during its testing process. Systematic, drift, and random errors constitute the major limitations to the accuracy of the calibration measurements.
Linchu, Han +3 more
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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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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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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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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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