Logistic Regression for simple, interpretable linear classification. Linear SVM for maximizing class separation in high-dimensional text space. Random Forest for capturing non-linear relationships and ...

Erfani, S.M., Rajasegarar, S., Karunasekera, S. and Leckie, C. (2016) High-Dimensional and Large-Scale Anomaly Detection Using a Linear One-Class SVM with Deep ...

To characterize sensory representations at the population-level, we used a linear Support Vector Machine (SVM). First, we focused on the representation of stimulus identity and decoded the chemical ...

Effective in High-Dimensional Spaces: SVM can handle high-dimensional data without overfitting, making it suitable for complex problems. Versatile: It can be used for both linear and non-linear ...

Machine learning models have significantly enriched the toolbox in the field of neuroimaging analysis. Among them, Support Vector Machines (SVM) have been one of the most popular models for supervised ...

Aram, K.Y., Lam, S.S. and Khasawneh, M.T. (2022) Linear Cost-Sensitive Max-Margin Embedded Feature Selection for Svm. Expert Systems with Applications, 197, Article ...

Hi, Thank you very much for creating such an incredible ecosystem of packages! I have encountered an issue when training a mlr3tuning::AutoTuner object which contains a classif.svm learner, either ...