Learn how the Least Squares Criterion determines the line of best fit for data analysis, enhancing predictive accuracy in finance, economics, and investing.

This is the code for SGIR, a semi-supervised framework for Graph Imbalanced Regression. Data imbalance is easily found in annotated data when the observations of certain continuous label values are ...

Abstract: The quadratic polynomial regression model with L2 regularization is developed by combining the nonlinear fitting ability of polynomial regression and the regularization feature of ridge ...

Abstract: The problem of denoising signals, defined over graph domains, using a regularization framework, is considered here. Using the $L^{2}$ norm, the optimum ...

Accurate predictions of earthquakes are crucial for disaster preparedness and risk mitigation. Conventional machine learning models like Random Forest, SVR, and XGBoost are frequently used for seismic ...