The specific type of kernel regression presented in this article is Nadaraya-Watson kernel regression using a radial basis function kernel. The term "kernel regression" by itself is ambiguous because ...

Learn some best practices and examples for selecting a kernel function that suits your data and objectives for your support vector machine (SVM) model.

This short tutorial aims at introducing support vector machine (SVM) methods from its mathematical formulation along with an efficient implementation in a few lines of Python!

Learn what kernel functions are, why they are important, and what are some of the most common and effective ones for support vector machine algorithms.

This paper represents a Kernel support vector machine based approach for the classification and detection of Parkinson's disease. In this paper, different kernel functions like linear, polynomial with ...

One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis. Methods: Hence, this paper provides a new hybrid machine learning model called genetic support vector ...

Finally, we provide a simple and user-friendly R function, and a tuned and parallelized version of the latter, which allow users to perform RR-BLUP of marker effects, GBLUP and RKHS regression with a ...