Visual Studio Magazine

Support Vector Regression with SGD Training Using C#

Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.
Journal of Medical Internet Research

Hepatic Steatosis Severity Prediction in Nonobese Individuals: Machine Learning Model Development and Validation

Six machine learning algorithms—k-nearest neighbors, naive Bayes, multilayer perceptron, random forest, support vector machine, and Extreme Gradient Boosting (XGBoost)—were developed using 10-fold ...
Journal of Medical Internet Research

Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease ...

Six ML algorithms (Extreme Gradient Boosting [XGBoost], logistic regression (LR), Light Gradient Boosting Machine [LightGBM], random forest [RF], support vector machine [SVM], and k-nearest neighbor ...
IEEE

Feature Selection Based on Twin Support Vector Regression

Abstract: Twin support vector regression (TSVR) is a regression algorithm based on the support vector regression (SVR) and the spirit of the support vector machine (TWSVM) . However, some feature ...
IEEE

The generalized LASSO

Abstract: In the last few years, the support vector machine (SVM) method has motivated new interest in kernel regression techniques. Although the SVM has been shown to exhibit excellent generalization ...