Background: Schizophrenia is a mental disorder that severely affects the perception and relations of individuals. Nowadays, this disease is diagnosed by psychiatrists based on psychiatric tests, which is highly dependent on their experience and knowledge. This study aimed to design a fully automated framework for the diagnosis of schizophrenia from electroencephalogram signals using advanced de...

The paper presents results from simulations based on real data, comparing several competing mean squared error of prediction (MSEP) estimators on principal components regression (PCR) and partial least squares regression (PLSR): leave-one-out crossvalidation, K-fold and adjusted K-fold cross-validation, the ordinary bootstrap estimate, the bootstrap smoothed cross-validation (BCV) estimate and ...

The k-fold cross-validation is commonly used to evaluate the effectiveness of SVMs with the selected hyper-parameters. It is known that the SVM k-fold cross-validation is expensive, since it requires training k SVMs. However, little work has explored reusing the h SVM for training the (h+ 1) SVM for improving the efficiency of k-fold cross-validation. In this paper, we propose three algorithms ...

This paper studies V -fold cross-validation for model selection in least-squares density estimation. The goal is to provide theoretical grounds for choosing V in order to minimize the least-squares loss of the selected estimator. We first prove a non-asymptotic oracle inequality for V -fold cross-validation and its bias-corrected version (V -fold penalization). In particular, this result implie...

We review accuracy estimation methods and compare the two most commonmethods cross validation and bootstrap Recent experimen tal results on arti cial data and theoretical re sults in restricted settings have shown that for selecting a good classi er from a set of classi ers model selection ten fold cross validation may be better than the more expensive leave one out cross validation We report o...

In this paper a new method for tuning regularisation parameters or other hyperparameters of a learning process (non-linear function estimation) is proposed, called robust cross-validation score function (CV S−fold). CV Robust S−fold is effective for dealing with outliers and nonGaussian noise distributions on the data. Illustrative simulation results are given to demonstrate that the CV S−fold ...

Multi-fold cross-validation is an established practice to estimate the error rate of a learning algorithm. Quantifying the variance reduction gains due to cross-validation has been challenging due to the inherent correlations introduced by the folds. In this work we introduce a new and weak measure called loss stability and relate the cross-validation performance to this measure; we also establ...

As seen in the bibliography, Adaptive Boosting (Adaboost) is one of the most known methods to increase the performance of an ensemble of neural networks. We introduce a new method based on Adaboost where we have applied Cross-Validation to increase the diversity of the ensemble. We have used CrossValidation over the whole learning set to generate an specific training set and validation set for ...

1 Motivations In machine learning, the standard measure of accuracy for models is the prediction error (PE), i.e. the expected loss on future examples. We consider here the i.i.d. regression or classification setups, where future examples are assumed to be independently sampled from the distribution that generated the training set. When the data distribution is unknown, PE cannot be computed. T...