GitHub

psoerensen/bayesian-linear-regression

This repository includes theoretical notes, slides, and hands-on R examples for exploring Bayesian Linear Regression. It introduces both classical and Bayesian regression methods, showing how to ...
Scientific Research Publishing

Financial Prediction Models in Banks: Combining Statistical Approaches and Machine Learning Algorithms ()

Felimban, R. (2025) Financial Prediction Models in Banks: Combining Statistical Approaches and Machine Learning Algorithms.
IEEE

Comparative Analysis of the Quality of Linear Regression on Principal Components Constructed by Robust and Classical Methods

Abstract: A linear regression model with correlated regressors having a contaminated Gaussian distribution is considered. To overcome multicollinearity, the method of constructing regression on ...
Digital Trends

Which Model Y trim should you buy? Model Y trims compared

The Tesla Model Y has been the most popular electric car for a few years now, and it makes sense. The Model Y is reasonably priced for an EV while offering a good range and an excellent software ...
marktechpost

PILOT: A New Machine Learning Algorithm for Linear Model Trees that is Fast, Regularized, Stable, and Interpretable

Prior to PILOT, fitting linear model trees was slow and prone to overfitting, especially with large datasets. Traditional regression trees struggled to capture linear relationships effectively. Linear ...
Frontiers

A framework for computer-aided high performance titanium alloy design based on machine learning

Titanium alloy exhibits exceptional performance and a wide range of applications, with the high performance serving as the foundation for the development. However, traditional material design methods ...
INSPIRE

Universal neural network potentials as descriptors: Towards scalable chemical property prediction using quantum and classical computers

Accurate prediction of diverse chemical properties is crucial for advancing molecular design and materials discovery. Here we present a versatile approach that uses the intermediate information of a ...
IEEE

Models LSSVR and PLSSVR With Heteroscedastic Gaussian Noise Characteristics and Its Application for Short-Term Wind-Speed Forecasting

Abstract: Proximal least squares support vector regression is a new regression machine designed by using regularization principle technology and least squares support vector regression. In this paper, ...