Results 271 to 280 of about 3,782,014 (296)
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Prediction of the Probable Errors of Predictions
Monthly Weather Review, 1985Abstract We propose here a method of “stochastic-dynamic” prediction that is computationally more efficient than integration of the full set of “second-moment” equations. This gain is achieved by omitting covariances between modes in different interacting triads, and by expressing intratriad covariances in terms of error variances, via the conditions ...
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Behavioural Brain Research, 2016
Violations of outcome expectancies have been proposed to account for error-related brain activity in the medial prefrontal cortex. The present study investigated whether early error monitoring processes are sensitive only to the expectancy of errors, or whether these processes also evaluate the significance of errors.
Martin E. Maier, Marco Steinhauser
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Violations of outcome expectancies have been proposed to account for error-related brain activity in the medial prefrontal cortex. The present study investigated whether early error monitoring processes are sensitive only to the expectancy of errors, or whether these processes also evaluate the significance of errors.
Martin E. Maier, Marco Steinhauser
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On the Asymptotic Behavior of the Prediction Error
Theory of Probability & Its Applications, 1964Let $\{ {x_j } \}$ be a stationary stochastic process in the wide sense which is regular, with spectral density function $f(\lambda )$. Denote by $\sigma _n^2 $ the mean square prediction error in predicting $x_0 $ by linear forms in $x_{ - 1} ,x_{ - 2} , \cdots ,x_{ - n} $.
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Error Prediction for Multi-Classification
Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/SAWN'05), 2005This paper describes an error prediction mechanism for multiclassification systems. First, a multiclassification system is constructed by combining a suite of two-class classifiers. While training, each sub-classifier does not utilize all the training data and the remaining data are used for testing purpose.
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Maximum likelihood and prediction error methods
Automatica, 1979Abstract The basic ideas behind the parameter estimation methods are discussed in a general setting. The application to estimation or parameters in dynamical systems is treated in detail using the prototype problem of estimating parameters in a continuous time system using discrete time measurements. Computational aspects are discussed.
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Prediction Errors Disrupt Hippocampal Representations and Update Episodic Memories
Proceedings of the National Academy of Sciences of the United States of America, 2021Morgan D. Barense +2 more
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A Novel Wind Speed Interval Prediction Based on Error Prediction Method
IEEE Transactions on Industrial Informatics, 2020Zhihuai Xiao, Pak Kin Wong, Chaoshun Li
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