Results 261 to 270 of about 8,137,182 (304)
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2021
In this paper, we attempt to deal with Cross Validation (henceforth CV). Initially, we define the loss functions which play an important role in CV methods and then, we present the ones we usually use in CV problems. We define the generalization error and present the context of the work as well.
Raimon Tolosana-Delgado, Ute Mueller
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In this paper, we attempt to deal with Cross Validation (henceforth CV). Initially, we define the loss functions which play an important role in CV methods and then, we present the ones we usually use in CV problems. We define the generalization error and present the context of the work as well.
Raimon Tolosana-Delgado, Ute Mueller
openaire +2 more sources
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
Statistics and computing, 2015Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior ...
Aki Vehtari, A. Gelman, Jonah Gabry
semanticscholar +1 more source
Physical Review Letters, 2013
We show that the information collected in the course of a generic quantum tomography experiment can be used for verifying experimenters' assumptions about the state preparation and measurement. In particular, systematic errors, such as drifts and instabilities inherent in the tomography setup, can be identified without the need for any specific ...
D, Mogilevtsev +3 more
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We show that the information collected in the course of a generic quantum tomography experiment can be used for verifying experimenters' assumptions about the state preparation and measurement. In particular, systematic errors, such as drifts and instabilities inherent in the tomography setup, can be identified without the need for any specific ...
D, Mogilevtsev +3 more
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Model Averaging Prediction by K-Fold Cross-Validation
Social Science Research Network, 2022,
Xinyu Zhang, Chu-An Liu
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Applied Psychological Measurement, 2014
The development of the kernel equating (KE) method enhanced the theory of observed-score equating. In KE, discrete test score distributions are converted into continuous distributions through the use of a Gaussian kernel. Traditionally, the optimal bandwidth for a Gaussian kernel was obtained by minimizing a penalty function.
Tie Liang, Alina A. von Davier
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The development of the kernel equating (KE) method enhanced the theory of observed-score equating. In KE, discrete test score distributions are converted into continuous distributions through the use of a Gaussian kernel. Traditionally, the optimal bandwidth for a Gaussian kernel was obtained by minimizing a penalty function.
Tie Liang, Alina A. von Davier
openaire +1 more source
Reliable Accuracy Estimates from k-Fold Cross Validation
IEEE Transactions on Knowledge and Data Engineering, 2020It is popular to evaluate the performance of classification algorithms by k-fold cross validation. A reliable accuracy estimate will have a relatively small variance, and several studies therefore suggested to repeatedly perform k-fold cross validation ...
Tzu-Tsung Wong, P. Yeh
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Journal of machine learning research, 2010
In regular statistical models, the leave-one-out cross-validation is asymptotically equivalent to the Akaike information criterion. However, since many learning machines are singular statistical models, the asymptotic behavior of the cross-validation ...
Sumio Watanabe
semanticscholar +1 more source
In regular statistical models, the leave-one-out cross-validation is asymptotically equivalent to the Akaike information criterion. However, since many learning machines are singular statistical models, the asymptotic behavior of the cross-validation ...
Sumio Watanabe
semanticscholar +1 more source
Journal of Mathematical Psychology, 2000
This paper gives a review of cross-validation methods. The original applications in multiple linear regression are considered first. It is shown how predictive accuracy depends on sample size and the number of predictor variables. Both two-sample and single-sample cross-validation indices are investigated. The application of cross-validation methods to
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This paper gives a review of cross-validation methods. The original applications in multiple linear regression are considered first. It is shown how predictive accuracy depends on sample size and the number of predictor variables. Both two-sample and single-sample cross-validation indices are investigated. The application of cross-validation methods to
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Computational materials science, 2020
The materials discovery problem usually aims to identify novel “outlier” materials with extremely low or high property values outside of the scope of all known materials. It can be mapped as an explorative prediction problem.
Zheng Xiong +5 more
semanticscholar +1 more source
The materials discovery problem usually aims to identify novel “outlier” materials with extremely low or high property values outside of the scope of all known materials. It can be mapped as an explorative prediction problem.
Zheng Xiong +5 more
semanticscholar +1 more source