Results 211 to 220 of about 1,045,360 (258)
Identification of Periodontal Disease Diagnostic Markers Via Data Cross-Validation. [PDF]
Int Dent JDu J, Liu Y, Luo Z, Wang M, Liu Y.
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Machine Learning-Based Ensemble Feature Selection and Nested Cross-Validation for miRNA Biomarker Discovery in Usher Syndrome. [PDF]
Bioengineering (Basel)Thelagathoti RK +6 more
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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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On Cross Validation for Model Selection
Neural Computation, 1999In response to Zhu and Rower (1996), a recent communication (Goutte, 1997) established that leave-one-out cross validation is not subject to the “no-free-lunch” criticism. Despite this optimistic conclusion, we show here that cross validation has very poor performances for the selection of linear models as compared to classic statistical tests.
Isabelle Rivals, Léon Personnaz
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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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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
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Communications in Statistics - Simulation and Computation, 2015
Data-based choice of the bandwidth is an important problem in kernel density estimation. The pseudo-likelihood and the least-squares cross-validation bandwidth selectors are well known, but widely criticized in the literature. For heavy-tailed distributions, the L1 distance between the pseudo-likelihood-based estimator and the density does not seem to ...
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Data-based choice of the bandwidth is an important problem in kernel density estimation. The pseudo-likelihood and the least-squares cross-validation bandwidth selectors are well known, but widely criticized in the literature. For heavy-tailed distributions, the L1 distance between the pseudo-likelihood-based estimator and the density does not seem to ...
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2005
K-fold cross validation is a commonly used technique which takes a set of m examples and partitions them into K equal-size sets (folds) of size m/K. For each set, a classifier is trained on the other sets.
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K-fold cross validation is a commonly used technique which takes a set of m examples and partitions them into K equal-size sets (folds) of size m/K. For each set, a classifier is trained on the other sets.
openaire +1 more source