The impact of the exponential Kernel’s bandwidth parameter on learning algorithms

Exponential kernels have been used considerably in statistics, machine learning, and artificial intelligence for tasks such as kernel principal component analysis (Kernel PCA), support vector machines(SVM), visualization, clustering, and pattern recognition. Selecting different bandwidth parameters for the exponential kernel can lead to varying insights about the data. Hence, understanding the theoretical impact of the bandwidth parameter is crucial. This paper investigates the influence of the exponential kernel's bandwidth parameter on the approximation of continuous operators by their empirical counterparts, supported by some experimental algorithms.