Results 1 to 10 of about 301,903 (177)
Forecast Error Calculation with Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE)
JINAV: Journal of Information and Visualization, 2020 Calculation errors in forecasting a data are very important from a forecasting process. The high level of forecasting accuracy will affect the level of confidence in forecasting decision making.
A. S. Ahmar
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Decomposition of the mean absolute error (MAE) into systematic and unsystematic components.
PLoS ONE, 2023 When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE).
Scott M Robeson, Cort J Willmott
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Mean Squared Error, Deconstructed
Journal of Advances in Modeling Earth Systems, 2021 As science becomes increasingly cross‐disciplinary and scientific models become increasingly cross‐coupled, standardized practices of model evaluation are more important than ever.
Thomas Over +2 more
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A mean-squared-error condition for weighting ionospheric delays in GNSS baselines
Journal of Geodesy, 2021 Although ionosphere-weighted GNSS parameter estimation is a popular technique for strengthening estimator performance in the presence of ionospheric delays, no provable rules yet exist that specify the needed weighting in dependence on ionospheric ...
Amir Khodabandeh +2 more
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Mean Squared Error (MSE) dan Penggunaannya
Jurnal Pemanfaatan Teknologi untuk Masyarakat: Jurnal Pengabdian MasyarakatMean Squared Error (MSE) adalah metrik evaluasi yang umum digunakan dalam statistik dan machine learning untuk mengukur seberapa akurat sebuah model regresi dalam memprediksi nilai numerik. MSE menghitung selisih antara nilai prediksi model dan nilai sebenarnya dari data, kemudian mengkuadratkan selisih tersebut agar tidak ada selisih yang bernilai ...
H. Nuha
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On the Value of Information and Mean Squared Error for Noisy Gaussian Models
IEEE Communications Letters, 2022 The relationship between the age of information (AoI) and the mean squared error (MSE) in optimisation problems has been widely investigated for various Gaussian Markov models.
Justin Coon +2 more
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Journal of Hydrology, 2009 The mean squared error (MSE) and the related normalization, the Nash-Sutcliffe efficiency (NSE), are the two criteria most widely used for calibration and evaluation of hydrological models with observed data. Here, we present a diagnostically interesting
Hoshin Vijai Gupta +2 more
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Performance evaluation of machine learning algorithms for predicting liquefaction-induced lateral displacement [PDF]
Scientific ReportsAccurate prediction of liquefaction-induced lateral displacement is essential for seismic risk assessment, resilient infrastructure design, and cost-effective mitigation.
Mahmood Ahmad +9 more
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A Competitive Mean-Squared Error Approach to Beamforming
IEEE Transactions on Signal Processing, 2007 We treat the problem of beamforming for signal estimation where the goal is to estimate a signal amplitude from a set of array observations. Conventional beamforming methods typically aim at maximizing the signal-to-interference-plus-noise ratio (SINR ...
Arye Nehorai +2 more
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Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation [PDF]
International Joint Conference on Artificial Intelligence, 2021 Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures.
Taehyeon Kim +4 more
semanticscholar +1 more source