The algorithm of k-fold cross validation is actively used to evaluate and compare machine
learning algorithms. However, it has several important deficiencies documented in the …

The numerical value of k in a k-fold cross-validation training technique of machine learning
predictive models is an essential element that impacts the model's performance. A right …

Myoelectric interface advancements have the potential to modify the use of wearable
prosthetics as limb replacements by using EMG signals to control active hand and arm …

Most machine learning researchers perform quantitative experiments to estimate
generalization error and compare the perforniance of different algorithms (in particular, their …

K-fold cross-validation produces variable estimates, whose variance cannot be estimated
unbiasedly. However, in practice, one would like to provide a figure related to the variability …

Cross-validation is an essential tool for evaluating how any given data analytic procedure
extends from a sample to the target population from which the sample is derived. It has seen …

We present a Bayesian approach for making statistical inference about the accuracy (or any
other score) of two competing algorithms which have been assessed via cross-validation on …

Most machine learning researchers perform quantitative experiments to estimate
generalization error and compare algorithm performances. In order to draw statistically …

ABSTRACT A large dataset is needed to obtain a large learning set for a suitable classifier,
while a large testing set is needed for a good estimate of the classifier's performance (ie …

In the machine learning field, the performance of a classifier is usually measured in terms of
prediction error. In most real-world problems, the error cannot be exactly calculated and it …