Least Square Regression for Prediction Problems in Machine Learning using R

Learning Rates of Least-Square Regularized Regression

This paper considers the regularized learning algorithm associated with the leastsquare loss and reproducing kernel Hilbert spaces. The target is the error analysis for the regression problem in learning theory. A novel regularization approach is presented, which yields satisfactory learning rates. The rates depend on the approximation property and the capacity of the reproducing kernel Hilbert...

Partial Least Square Regression PLS-Regression

PLS regression is a recent technique that generalizes and combines features from principal component analysis and multiple regression. Its goal is to predict or analyze a set of dependent variables from a set of independent variables or predictors. This prediction is achieved by extracting from the predictors a set of orthogonal factors called latent variables which have the best predictive pow...

Orthogonal Least Square with Boosting for Regression

Data-driven approach to machine condition prognosis using least square regression trees

Machine fault prognosis techniques have been profoundly considered in the recent time due to their substantial profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are precisely forecasted before they reach the failure thresholds. In this work, we propose an appro...

Credit Risk Classification Using Kernel Logistic Regression-least Square Support Vector Machine

Kernel Logistic Regression (KLR) is one of the statistical models that have been proposed for classification in the machine learning and data mining communities, and also one of the effective methodologies in the kernel-machine techniques. The parameters of KLR model are usually fitted by the solution of a convex optimization problem that can be found using the well known Iteratively Reweighted...