Results 161 to 170 of about 5,039,055 (202)
Some of the next articles are maybe not open access.
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
openaire +3 more sources
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
openaire +3 more sources
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
openaire +3 more sources
Population Validity and Cross-Validity
Educational and Psychological Measurement, 2007Applications of distribution theory for the squared multiple correlation coefficient and the squared cross-validation coefficient are reviewed, and computer programs for these applications are made available. The applications include confidence intervals, hypothesis testing, and sample size selection.
James Algina, H.J. Keselman
openaire +1 more source
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 ...
openaire +1 more source
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 ...
openaire +1 more source
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.
openaire +1 more source
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
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
openaire +1 more source
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
Validity and Cross-Validity in HCI Publications
2007Papers in HCI play different roles, whether to inspire, solve industrial problems or further the science of HCI. There is a potential conflict between the different views, and a danger that different forms of validity are assumed by author and reader-- deliberately or accidentally. This paper reviews some of the issues in this complex area and makes
openaire +2 more sources
Nested cross-validation when selecting classifiers is overzealous for most practical applications
Expert Systems With Applications, 2021Jacques Wainer, Gavin Cawley
exaly