RBFkernels
RBF kernels, also known as Radial Basis Function kernels, are a popular type of kernel function used in various machine learning algorithms, particularly support vector machines (SVMs) and Gaussian processes. The RBF kernel measures the similarity between two data points in a high-dimensional feature space, effectively enabling non-linear decision boundaries in input space.
Mathematically, the RBF kernel between two points \( x \) and \( x' \) is defined as:
\[ K(x, x') = \exp(-\gamma \|x - x'\|^2) \]
where \( \|x - x'\|^2 \) is the squared Euclidean distance between the points, and \( \gamma \) (gamma) is a
The RBF kernel is popular due to its ability to handle non-linear relationships in data without explicitly
Choosing the right value for the \( \gamma \) parameter and other hyperparameters often involves cross-validation. Proper tuning