Definition Cross-Validation is a statistical method of evaluating and comparing learning algorithms by dividing data into two segments: one used to learn or train a model and the other used to validate the model. In typical cross-validation, the training and validation sets must cross-over in successive rounds such that each data point has a chance of being validated against. The basic form of ...

there are three major strategies to form neural network ensembles. the simplest one is the cross validation strategy in which all members are trained with the same training data. bagging and boosting strategies pro-duce perturbed sample from training data. this paper provides an ideal model based on two important factors: activation function and number of neurons in the hidden layer and based u...

Our first comment is on the optimization of the model parameter τ controlling the amount of noise in the augmented bootstrap method. In a supervised prediction problem, τ can and should be optimized using, e.g., a cross-validation (CV) procedure, as suggested by the authors. If the prediction accuracy is itself evaluated by cross-validation or a related approach, this yields a nested cross-vali...

MOTIVATION Numerous competing algorithms for prediction in high-dimensional settings have been developed in the statistical and machine-learning literature. Learning algorithms and the prediction models they generate are typically evaluated on the basis of cross-validation error estimates in a few exemplary datasets. However, in most applications, the ultimate goal of prediction modeling is to ...

Suppose that, for a learning task, we have to select one hypothesis out of a set of hypotheses (that may, for example, have been generated by multiple applications of a randomized learning algorithm). A common approach is to evaluate each hypothesis in the set on some previously unseen cross-validation data, and then to select the hypothesis that had the lowest cross-validation error. But when ...

In most cases of QSAR modelling the final model used to make predictions, is not known a priori but has to be selected in a data driven fashion (e.g. selection of principal components, variable selection, selection of the best mathematical modelling technique). Reliable estimation of externally validated prediction errors under this model uncertainty is still a challenge in chemoinformatics. To...

the use of third-party logistics (3pl) providers is regarded as new strategy in logistics management. the relationships by considering 3pl are sometimes more complicated than any classical logistics supplier relationships. these relationships have taken into account as a well-known way to highlight organizations' flexibilities to regard rapidly uncertain market conditions, follow core competenc...

Cross-validation is one of the most widely used techniques, in estimating the Generalization Error of classification algorithms. Though several empirical studies have been conducted, to study the behavior of this method in the past, none of them clearly elucidate the reasons behind the observed behavior. In this paper we study the behavior of the moments (i.e. expected value and variance) of th...

We describe three language-independent methods for the task of answer validation. All methods are based on a scoring mechanism that reflects the degree of similarity between the question-answer pairs and the supporting text. We evaluate the proposed methods when using various string similarity metrics, such as exact matching, Levenshtein, Jaro and Jaro-Winkler. In addition to this baseline appr...