The problem of adversarial robustness has been studied extensively for neural networks.
However, for boosted decision trees and decision stumps there are almost no results, even …

Ranking problems, also known as preference learning problems, define a widely spread
class of statistical learning problems with many applications, including fraud detection …

Nonparametric regression models offer a way to understand and quantify relationships
between variables without having to identify an appropriate family of possible regression …

In this study, different distinct approaches of machine learning (ML) including Multi-layer
perceptron (MLP), Gradient Boosting with DT (GBDT), and Gaussian process regression …

A multi-sensor water quality monitoring system incorporating an UV/Vis spectrometer and a
turbidimeter was used to monitor the Chemical Oxygen Demand (COD), Total Suspended …

Boosting partial least squares (PLS) has been used for regression to improve the predictive
accuracy of PLS models, however, there are still problems when the outliers exist in the …

The spatial lag specification is often used in spatial econometrics. The choice of an
appropriate spatial weighting matrix is an important outstanding methodological problem in …

Many of the popular nonlinear time series models require a priori the choice of parametric
functions which are assumed to be appropriate in specific applications. This approach is …

This article examines the role and the efficiency of nonconvex loss functions for binary
classification problems. In particular, we investigate how to design adaptive and effective …

For the risk, progression, and response to treatment of many complex diseases, it has been
increasingly recognized that genetic interactions (including gene–gene and gene …