Regularized linear models are important classification methods for high dimensional problems, where regularized linear classifiers are often preferred due to their ability to avoid overfitting. The degree of freedom of the model is determined by a regularization parameter, which is typically selected using counting based approaches, such as K-fold cross-validation. For large data, this can be v...

BACKGROUND We consider both univariate- and multivariate-based feature selection for the problem of binary classification with microarray data. The idea is to determine whether the more sophisticated multivariate approach leads to better misclassification error rates because of the potential to consider jointly significant subsets of genes (but without overfitting the data). METHODS We presen...

We provide a method for estimating the generalization error of a bag using out-of-bag estimates. In bagging, each predictor (single hypothesis) is learned from a bootstrap sample of the training examples; the output of a bag (a set of predictors) on an example is determined by voting. The outof-bag estimate is based on recording the votes of each predictor on those training examples omitted fro...

Methods, such as holdout, random subsampling, k-fold cross-validation, and bootstrap, for making error estimation are discussed. Also considered are general techniques, such as bagging and boosting, for increasing model accuracy. Directory • Table of

Knowledge of the concentration of total suspended sediment (TSS) in coastal waters is of significance to marine environmental monitoring agencies to determine the turbidity of water that serve as a proxy to estimate the availability of light at depth for benthic habitats. TSS models applicable to data collected by satellite sensors can be used to determine TSS with reasonable accuracy and of ad...

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...

Background: Skinfold-thickness measurements are commonly obtained for the indirect assessment of body composition. Objective: We developed new skinfold-thickness equations by using a 4-compartment model as the reference. Additionally, we compared our new equations with the Durnin and Womersley and Jackson and Pollock skinfold-thickness equations to evaluate each equation’s validity and precisio...

The choice of a smoothing parameter or bandwidth is crucial when applying nonparametric regression estimators. In nonparametric mean regression various methods for bandwidth selection exists. But in nonparametric quantile regression bandwidth choice is still an unsolved problem. In this paper a selection procedure for local varying bandwidths based on the asymptotic mean squared error (MSE) of ...

Non-quadratic regularizers, in particular the ` 1 norm regularizer can yield sparse solutions that generalize well. In this work we propose the Generalized Subspace Information Criterion (GSIC) that allows to predict the generalization error for this useful family of regularizers. We show that under some technical assumptions GSIC is an asymptotically unbiased estimator of the generalization er...

This paper introduces a detection methodology for recognition technologies in speech for which it is difficult to obtain an abundance of non-target classes. An example is language recognition, where we would like to be able to measure the detection capability of a single target language without confounding with the modeling capability of non-target languages. The evaluation framework is based o...