Mika et al. [1] apply the “kernel trick” to obtain a non-linear variant of Fisher’s linear discriminant analysis method, demonstrating state-of-the-art performance on a range of benchmark datasets. We show that leave-one-out cross-validation of kernel Fisher discriminant classifiers can be implemented with a computational complexity of only O(l3) operations rather than the O(l4) of a näıve impl...

since the change of land use accrued in the iran, especially in northern iran, this research aims tocompare the spatial variability of soil properties in three adjacent land uses including cultivated by wheat lands, grazing lands and forest lands covered by juniperus sp,  fagus orientalis, quercus castanifolia, and acer velotinum species in kiasar region, mazandaran province, northern iran. som...

Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d. observations from the true density among a collection of candidate density estimators. General examples are the selection of a model indexing a maximum likelihood estimator, and the selection of a bandwidth indexing a nonparametric (e.g. kernel) density estimator. In this article, we estab...

Modern steganalysis is a combination of a feature space design and a supervised binary classification. In this report, we assume that the feature space has been already constructed, i.e., the steganalyst has a set of training features and needs to train a binary classifier. Any machine learning tool can be used for this task and its parameters can be tuned through cross-validation, a standard a...

The Watanabe-Akaike information criterion (WAIC) and cross-validation are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model. WAIC is based on the series expansion of leave-one-out cross-validation (LOO), and asymptotically they are equal. With finite data, WAIC and cross-validation address different predictive questions and thus it is useful to be a...

In this paper new robust methods for tuning regularization parameters or other tuning parameters of a learning process for non-linear function estimation are proposed: repeated robust cross-validation score functions (repeated-CV Robust V −fold) and a robust generalized cross-validation score function (GCVRobust). Both methods are effective for dealing with outliers and non-Gaussian noise distr...

Objective The aim of this study was to compare the diagnostic performance convolutional neural networks (CNNs) with human observers for detection simulated periapical lesions on radiographs. Study Design Ten sockets were prepared in bovine ribs. Periapical defects 3 sizes sequentially created. radiographs acquired each socket no lesion and size a photostimulable storage phosphor system. Radiogr...

In recent years, many studies have been published on data collected from social media, especially microblogs such as Twitter. However, rather few of these studies have considered evaluation methodologies that take into account the statistically dependent nature of such data, which breaks the theoretical conditions for using cross-validation. Despite concerns raised in the past about using cross...