Results 231 to 240 of about 561,529 (268)
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Quantization Error in Predictive Coders
IEEE Transactions on Communications, 1975Predictive coders have been suggested for use as analog data compression devices. Exact expressions for reconstructed signal error have been rare in the literature. In fact most results reported in the literature are based on the assumption of Gaussian statistics for prediction error.
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Gaining on reward prediction errors
Nature Neuroscience, 2016In this issue of Nature Neuroscience, Eshel et al. characterize the homogeneity with which individual dopamine neurons encode reward prediction error, a teaching signal that is thought to be crucial for associative learning.
Nathan F, Parker, Ilana B, Witten
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Minimising the Context Prediction Error
2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring, 2007Context prediction mechanisms proactively provide information on future contexts. Due to this knowledge novel applications become possible that provide services with proactive knowledge to users. The most serious problem of context prediction mechanisms lies in a basic property of prediction itself. A prediction is always a guess.
Stephan Sigg +2 more
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Reward positivity: Reward prediction error or salience prediction error?
Psychophysiology, 2016AbstractThe reward positivity is a component of the human ERP elicited by feedback stimuli in trial‐and‐error learning and guessing tasks. A prominent theory holds that the reward positivity reflects a reward prediction error signal that is sensitive to outcome valence, being larger for unexpected positive events relative to unexpected negative events (
Sepideh, Heydari, Clay B, Holroyd
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On the Error of Prediction of a Time Series
Biometrika, 1972Abstract : Parametric and nonparametric procedures for the prediction of a time series are discussed. In each case the increase in the mean squared error of prediction over its minimum level due to the use of estimated spectra is assessed. The fitting of simple parametric models as approximations is also discussed. (Author)
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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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